Google Professional Cloud Architect Practice Questions Free – 50 Exam-Style Questions to Sharpen Your Skills
Are you preparing for the Google Professional Cloud Architect certification exam? Kickstart your success with our Google Professional Cloud Architect Practice Questions Free – a carefully selected set of 50 real exam-style questions to help you test your knowledge and identify areas for improvement.
Practicing with Google Professional Cloud Architect practice questions free gives you a powerful edge by allowing you to:
- Understand the exam structure and question formats
- Discover your strong and weak areas
- Build the confidence you need for test day success
Below, you will find 50 free Google Professional Cloud Architect practice questions designed to match the real exam in both difficulty and topic coverage. They’re ideal for self-assessment or final review. You can click on each Question to explore the details.
Your company has decided to build a backup replica of their on-premises user authentication PostgreSQL database on Google Cloud Platform. The database is 4 TB, and large updates are frequent. Replication requires private address space communication. Which networking approach should you use?
A. Google Cloud Dedicated Interconnect
B. Google Cloud VPN connected to the data center network
C. A NAT and TLS translation gateway installed on-premises
D. A Google Compute Engine instance with a VPN server installed connected to the data center network
Company Overview - TerramEarth manufactures heavy equipment for the mining and agricultural industries: about 80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive. Company background - TerramEarth was formed in 1946, when several small, family owned companies combined to retool after World War II. The company cares about their employees and customers and considers them to be extended members of their family. TerramEarth is proud of their ability to innovate on their core products and find new markets as their customers' needs change. For the past 20 years, trends in the industry have been largely toward increasing productivity by using larger vehicles with a human operator. Solution Concept - There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second. Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced. The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules. Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second with 22 hours of operation per day, Terram Earth collects a total of about 9 TB/day from these connected vehicles. Existing Technical Environment -TerramEarth's existing architecture is composed of Linux-based systems that reside in a data center. These systems gzip CSV files from the field and upload via FTP, transform and aggregate them, and place the data in their data warehouse. Because this process takes time, aggregated reports are based on data that is 3 weeks old. With this data, TerramEarth has been able to preemptively stock replacement parts and reduce unplanned downtime of their vehicles by 60%. However, because the data is stale, some customers are without their vehicles for up to 4 weeks while they wait for replacement parts. Business Requirements - Decrease unplanned vehicle downtime to less than 1 week, without increasing the cost of carrying surplus inventory Support the dealer network with more data on how their customers use their equipment to better position new products and services Have the ability to partner with different companies `" especially with seed and fertilizer suppliers in the fast-growing agricultural business `" to create compelling joint offerings for their customers. CEO Statement - We have been successful in capitalizing on the trend toward larger vehicles to increase the productivity of our customers. Technological change is occurring rapidly, and TerramEarth has taken advantage of connected devices technology to provide our customers with better services, such as our intelligent farming equipment. With this technology, we have been able to increase farmers' yields by 25%, by using past trends to adjust how our vehicles operate. These advances have led to the rapid growth of our agricultural product line, which we expect will generate 50% of our revenues by 2020. CTO Statement - Our competitive advantage has always been in the manufacturing process, with our ability to build better vehicles for lower cost than our competitors. However, new products with different approaches are constantly being developed, and I'm concerned that we lack the skills to undergo the next wave of transformations in our industry. Unfortunately, our CEO doesn't take technology obsolescence seriously and he considers the many new companies in our industry to be niche players. My goals are to build our skills while addressing immediate market needs through incremental innovations. Operational parameters such as oil pressure are adjustable on each of TerramEarth's vehicles to increase their efficiency, depending on their environmental conditions. Your primary goal is to increase the operating efficiency of all 20 million cellular and unconnected vehicles in the field. How can you accomplish this goal?
A. Have you engineers inspect the data for patterns, and then create an algorithm with rules that make operational adjustments automatically
B. Capture all operating data, train machine learning models that identify ideal operations, and run locally to make operational adjustments automatically
C. Implement a Google Cloud Dataflow streaming job with a sliding window, and use Google Cloud Messaging (GCM) to make operational adjustments automatically
D. Capture all operating data, train machine learning models that identify ideal operations, and host in Google Cloud Machine Learning (ML) Platform to make operational adjustments automatically
You have an application deployed on Google Kubernetes Engine using a Deployment named echo-deployment. The deployment is exposed using a Service called echo-service. You need to perform an update to the application with minimal downtime to the application. What should you do?
A. Use kubectl set image deployment/echo-deployment
B. Use the rolling update functionality of the Instance Group behind the Kubernetes cluster
C. Update the deployment yaml file with the new container image. Use kubectl delete deployment/echo-deployment and kubectl create ג€”f
D. Update the service yaml file which the new container image. Use kubectl delete service/echo-service and kubectl create ג€”f
You write a Python script to connect to Google BigQuery from a Google Compute Engine virtual machine. The script is printing errors that it cannot connect to BigQuery. What should you do to fix the script?
A. Install the latest BigQuery API client library for Python
B. Run your script on a new virtual machine with the BigQuery access scope enabled
C. Create a new service account with BigQuery access and execute your script with that user
D. Install the bq component for gcloud with the command gcloud components install bq.
Your organization has a 3-tier web application deployed in the same network on Google Cloud Platform. Each tier (web, API, and database) scales independently of the others. Network traffic should flow through the web to the API tier and then on to the database tier. Traffic should not flow between the web and the database tier. How should you configure the network?
A. Add each tier to a different subnetwork
B. Set up software based firewalls on individual VMs
C. Add tags to each tier and set up routes to allow the desired traffic flow
D. Add tags to each tier and set up firewall rules to allow the desired traffic flow
Your customer is moving their corporate applications to Google Cloud Platform. The security team wants detailed visibility of all projects in the organization. You provision the Google Cloud Resource Manager and set up yourself as the org admin. What Google Cloud Identity and Access Management (Cloud IAM) roles should you give to the security team?
A. Org viewer, project owner
B. Org viewer, project viewer
C. Org admin, project browser
D. Project owner, network admin
Your company is developing a new application that will allow globally distributed users to upload pictures and share them with other selected users. The application will support millions of concurrent users. You want to allow developers to focus on just building code without having to create and maintain the underlying infrastructure. Which service should you use to deploy the application?
A. App Engine
B. Cloud Endpoints
C. Compute Engine
D. Google Kubernetes Engine
Your company is migrating its on-premises data center into the cloud. As part of the migration, you want to integrate Google Kubernetes Engine (GKE) for workload orchestration. Parts of your architecture must also be PCI DSS-compliant. Which of the following is most accurate?
A. App Engine is the only compute platform on GCP that is certified for PCI DSS hosting.
B. GKE cannot be used under PCI DSS because it is considered shared hosting.
C. GKE and GCP provide the tools you need to build a PCI DSS-compliant environment.
D. All Google Cloud services are usable because Google Cloud Platform is certified PCI-compliant.
Company Overview - Dress4Win is a web-based company that helps their users organize and manage their personal wardrobe using a website and mobile application. The company also cultivates an active social network that connects their users with designers and retailers. They monetize their services through advertising, e-commerce, referrals, and a premium app model. Company Background - Dress4Win's application has grown from a few servers in the founder's garage to several hundred servers and appliances in a collocated data center. However, the capacity of their infrastructure is now insufficient for the application's rapid growth. Because of this growth and the company's desire to innovate faster, Dress4Win is committing to a full migration to a public cloud. Solution Concept - For the first phase of their migration to the cloud, Dress4Win is considering moving their development and test environments. They are also considering building a disaster recovery site, because their current infrastructure is at a single location. They are not sure which components of their architecture they can migrate as is and which components they need to change before migrating them. Existing Technical Environment - The Dress4Win application is served out of a single data center location. Databases: - MySQL - user data, inventory, static data - Redis - metadata, social graph, caching Application servers: - Tomcat - Java micro-services - Nginx - static content - Apache Beam - Batch processing Storage appliances: - iSCSI for VM hosts - Fiber channel SAN - MySQL databases - NAS - image storage, logs, backups Apache Hadoop/Spark servers: - Data analysis - Real-time trending calculations MQ servers: - Messaging - Social notifications - Events Miscellaneous servers: - Jenkins, monitoring, bastion hosts, security scanners Business Requirements - Build a reliable and reproducible environment with scaled parity of production.Improve security by defining and adhering to a set of security and Identity and Access Management (IAM) best practices for cloud. Improve business agility and speed of innovation through rapid provisioning of new resources. Analyze and optimize architecture for performance in the cloud. Migrate fully to the cloud if all other requirements are met. Technical Requirements - Evaluate and choose an automation framework for provisioning resources in cloud. Support failover of the production environment to cloud during an emergency. Identify production services that can migrate to cloud to save capacity. Use managed services whenever possible. Encrypt data on the wire and at rest. Support multiple VPN connections between the production data center and cloud environment. CEO Statement - Our investors are concerned about our ability to scale and contain costs with our current infrastructure. They are also concerned that a new competitor could use a public cloud platform to offset their up-front investment and freeing them to focus on developing better features. CTO Statement - We have invested heavily in the current infrastructure, but much of the equipment is approaching the end of its useful life. We are consistently waiting weeks for new gear to be racked before we can start new projects. Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80% of our capacity is sitting idle. CFO Statement - Our capital expenditure is now exceeding our quarterly projections. Migrating to the cloud will likely cause an initial increase in spending, but we expect to fully transition before our next hardware refresh cycle. Our total cost of ownership (TCO) analysis over the next 5 years puts a cloud strategy between 30 to 50% lower than our current model. Dress4Win would like to become familiar with deploying applications to the cloud by successfully deploying some applications quickly, as is. They have asked for your recommendation. What should you advise?
A. Identify self-contained applications with external dependencies as a first move to the cloud.
B. Identify enterprise applications with internal dependencies and recommend these as a first move to the cloud.
C. Suggest moving their in-house databases to the cloud and continue serving requests to on-premise applications.
D. Recommend moving their message queuing servers to the cloud and continue handling requests to on-premise applications.
Company Overview - Dress4Win is a web-based company that helps their users organize and manage their personal wardrobe using a website and mobile application. The company also cultivates an active social network that connects their users with designers and retailers. They monetize their services through advertising, e-commerce, referrals, and a premium app model. Company Background - Dress4Win's application has grown from a few servers in the founder's garage to several hundred servers and appliances in a collocated data center. However, the capacity of their infrastructure is now insufficient for the application's rapid growth. Because of this growth and the company's desire to innovate faster, Dress4Win is committing to a full migration to a public cloud. Solution Concept - For the first phase of their migration to the cloud, Dress4Win is considering moving their development and test environments. They are also considering building a disaster recovery site, because their current infrastructure is at a single location. They are not sure which components of their architecture they can migrate as is and which components they need to change before migrating them. Existing Technical Environment - The Dress4Win application is served out of a single data center location. Databases: - MySQL - user data, inventory, static data - Redis - metadata, social graph, caching Application servers: - Tomcat - Java micro-services - Nginx - static content - Apache Beam - Batch processing Storage appliances: - iSCSI for VM hosts - Fiber channel SAN - MySQL databases - NAS - image storage, logs, backups Apache Hadoop/Spark servers: - Data analysis - Real-time trending calculations MQ servers: - Messaging - Social notifications - Events Miscellaneous servers: - Jenkins, monitoring, bastion hosts, security scanners Business Requirements - Build a reliable and reproducible environment with scaled parity of production.
A. Store image files in a Google Cloud Storage bucket. Use Google Cloud Datastore to maintain metadata that maps each customer’s ID and their image files.
B. Store image files in a Google Cloud Storage bucket. Add custom metadata to the uploaded images in Cloud Storage that contains the customer’s unique ID.
C. Use a distributed file system to store customers’ images. As storage needs increase, add more persistent disks and/or nodes. Assign each customer a unique ID, which sets each file’s owner attribute, ensuring privacy of images.
D. Use a distributed file system to store customers’ images. As storage needs increase, add more persistent disks and/or nodes. Use a Google Cloud SQL database to maintain metadata that maps each customer’s ID to their image files.
Your customer wants to do resilience testing of their authentication layer. This consists of a regional managed instance group serving a public REST API that reads from and writes to a Cloud SQL instance. What should you do?
A. Engage with a security company to run web scrapers that look your for users’ authentication data om malicious websites and notify you if any is found.
B. Deploy intrusion detection software to your virtual machines to detect and log unauthorized access.
C. Schedule a disaster simulation exercise during which you can shut off all VMs in a zone to see how your application behaves.
D. Configure a read replica for your Cloud SQL instance in a different zone than the master, and then manually trigger a failover while monitoring KPIs for our REST API.
Company overview - Helicopter Racing League (HRL) is a global sports league for competitive helicopter racing. Each year HRL holds the world championship and several regional league competitions where teams compete to earn a spot in the world championship. HRL offers a paid service to stream the races all over the world with live telemetry and predictions throughout each race. Solution concept - HRL wants to migrate their existing service to a new platform to expand their use of managed AI and ML services to facilitate race predictions. Additionally, as new fans engage with the sport, particularly in emerging regions, they want to move the serving of their content, both real-time and recorded, closer to their users. Existing technical environment - HRL is a public cloud-first company; the core of their mission-critical applications runs on their current public cloud provider. Video recording and editing is performed at the race tracks, and the content is encoded and transcoded, where needed, in the cloud. Enterprise-grade connectivity and local compute is provided by truck-mounted mobile data centers. Their race prediction services are hosted exclusively on their existing public cloud provider. Their existing technical environment is as follows: Existing content is stored in an object storage service on their existing public cloud provider. Video encoding and transcoding is performed on VMs created for each job. Race predictions are performed using TensorFlow running on VMs in the current public cloud provider. Business requirements - HRL's owners want to expand their predictive capabilities and reduce latency for their viewers in emerging markets. Their requirements are: Support ability to expose the predictive models to partners. Increase predictive capabilities during and before races: ג—‹ Race results ג—‹ Mechanical failures ג—‹ Crowd sentiment Increase telemetry and create additional insights. Measure fan engagement with new predictions. Enhance global availability and quality of the broadcasts. Increase the number of concurrent viewers. Minimize operational complexity. Ensure compliance with regulations. Create a merchandising revenue stream. Technical requirements - Maintain or increase prediction throughput and accuracy. Reduce viewer latency. Increase transcoding performance. Create real-time analytics of viewer consumption patterns and engagement. Create a data mart to enable processing of large volumes of race data. Executive statement - Our CEO, S. Hawke, wants to bring high-adrenaline racing to fans all around the world. We listen to our fans, and they want enhanced video streams that include predictions of events within the race (e.g., overtaking). Our current platform allows us to predict race outcomes but lacks the facility to support real-time predictions during races and the capacity to process season-long results. For this question, refer to the Helicopter Racing League (HRL) case study. Your team is in charge of creating a payment card data vault for card numbers used to bill tens of thousands of viewers, merchandise consumers, and season ticket holders. You need to implement a custom card tokenization service that meets the following requirements: * It must provide low latency at minimal cost. * It must be able to identify duplicate credit cards and must not store plaintext card numbers. * It should support annual key rotation. Which storage approach should you adopt for your tokenization service?
A. Store the card data in Secret Manager after running a query to identify duplicates.
B. Encrypt the card data with a deterministic algorithm stored in Firestore using Datastore mode.
C. Encrypt the card data with a deterministic algorithm and shard it across multiple Memorystore instances.
D. Use column-level encryption to store the data in Cloud SQL.
You need to deploy an application to Google Cloud. The application receives traffic via TCP and reads and writes data to the filesystem. The application does not support horizontal scaling. The application process requires full control over the data on the file system because concurrent access causes corruption. The business is willing to accept a downtime when an incident occurs, but the application must be available 24/7 to support their business operations. You need to design the architecture of this application on Google Cloud. What should you do?
A. Use a managed instance group with instances in multiple zones, use Cloud Filestore, and use an HTTP load balancer in front of the instances.
B. Use a managed instance group with instances in multiple zones, use Cloud Filestore, and use a network load balancer in front of the instances.
C. Use an unmanaged instance group with an active and standby instance in different zones, use a regional persistent disk, and use an HTTP load balancer in front of the instances.
D. Use an unmanaged instance group with an active and standby instance in different zones, use a regional persistent disk, and use a network load balancer in front of the instances.
Your customer is receiving reports that their recently updated Google App Engine application is taking approximately 30 seconds to load for some of their users. This behavior was not reported before the update. What strategy should you take?
A. Work with your ISP to diagnose the problem
B. Open a support ticket to ask for network capture and flow data to diagnose the problem, then roll back your application
C. Roll back to an earlier known good release initially, then use Stackdriver Trace and Logging to diagnose the problem in a development/test/staging environment
D. Roll back to an earlier known good release, then push the release again at a quieter period to investigate. Then use Stackdriver Trace and Logging to diagnose the problem
You have a Compute Engine application that you want to autoscale when total memory usage exceeds 80%. You have installed the Cloud Monitoring agent and configured the autoscaling policy as follows: ✑ Metric identifier: agent.googleapis.com/memory/percent_used ✑ Filter: metric.label.state = 'used' ✑ Target utilization level: 80 ✑ Target type: GAUGE You observe that the application does not scale under high load. You want to resolve this. What should you do?
A. Change the Target type to DELTA_PER_MINUTE.
B. Change the Metric identifier to agent.googleapis.com/memory/bytes_used.
C. Change the filter to metric.label.state = ‘used’ AND metric.label.state = ‘buffered’ AND metric.label.state = ‘cached’ AND metric.label.state = ‘slab’.
D. Change the filter to metric.label.state = ‘free’ and the Target utilization to 20.
Your company is planning to upload several important files to Cloud Storage. After the upload is completed, they want to verify that the uploaded content is identical to what they have on-premises. You want to minimize the cost and effort of performing this check. What should you do?
A. 1. Use Linux shasum to compute a digest of files you want to upload. 2. Use gsutil -m to upload all the files to Cloud Storage. 3. Use gsutil cp to download the uploaded files. 4. Use Linux shasum to compute a digest of the downloaded files. 5. Compare the hashes.
B. 1. Use gsutil -m to upload the files to Cloud Storage. 2. Develop a custom Java application that computes CRC32C hashes. 3. Use gsutil ls -L gs://[YOUR_BUCKET_NAME] to collect CRC32C hashes of the uploaded files. 4. Compare the hashes.
C. 1. Use gsutil -m to upload all the files to Cloud Storage. 2. Use gsutil cp to download the uploaded files. 3. Use Linux diff to compare the content of the files.
D. 1. Use gsutil -m to upload the files to Cloud Storage. 2. Use gsutil hash -c FILE_NAME to generate CRC32C hashes of all on-premises files. 3. Use gsutil ls -L gs://[YOUR_BUCKET_NAME] to collect CRC32C hashes of the uploaded files. 4. Compare the hashes.
You are monitoring Google Kubernetes Engine (GKE) clusters in a Cloud Monitoring workspace. As a Site Reliability Engineer (SRE), you need to triage incidents quickly. What should you do?
A. Navigate the predefined dashboards in the Cloud Monitoring workspace, and then add metrics and create alert policies.
B. Navigate the predefined dashboards in the Cloud Monitoring workspace, create custom metrics, and install alerting software on a Compute Engine instance.
C. Write a shell script that gathers metrics from GKE nodes, publish these metrics to a Pub/Sub topic, export the data to BigQuery, and make a Data Studio dashboard.
D. Create a custom dashboard in the Cloud Monitoring workspace for each incident, and then add metrics and create alert policies.
Company Overview - Mountkirk Games makes online, session-based, multiplayer games for the most popular mobile platforms. They build all of their games using some server-side integration. Historically, they have used cloud providers to lease physical servers. Due to the unexpected popularity of some of their games, they have had problems scaling their global audience, application servers MySQL databases, and analytics tools. Their current model is to write game statistics to files and send them through an ETL tool that loads them into a centralized MySQL database for reporting. Solution Concept - Mountkirk Games is building a new game, which they expect to be very popular. They plan to deploy the game's backend on Google Compute Engine so they can capture streaming metrics run intensive analytics, and take advantage of its autoscaling server environment and integrate with a managed NoSQL database. Business Requirements - Increase to a global footprint Improve uptime `" downtime is loss of players Increase efficiency of the cloud resources we use Reduce latency to all customers Technical Requirements - Requirements for Game Backend Platform 1. Dynamically scale up or down based on game activity 2. Connect to a managed NoSQL database service 3. Run customize Linux distro Requirements for Game Analytics Platform 1. Dynamically scale up or down based on game activity 2. Process incoming data on the fly directly from the game servers 3. Process data that arrives late because of slow mobile networks 4. Allow SQL queries to access at least 10 TB of historical data 5. Process files that are regularly uploaded by users' mobile devices 6. Use only fully managed services CEO Statement - Our last successful game did not scale well with our previous cloud provider, resulting in lower user adoption and affecting the game's reputation. Our investors want more key performance indicators (KPIs) to evaluate the speed and stability of the game, as well as other metrics that provide deeper insight into usage patterns so we can adapt the game to target users. CTO Statement - Our current technology stack cannot provide the scale we need, so we want to replace MySQL and move to an environment that provides autoscaling, low latency load balancing, and frees us up from managing physical servers. CFO Statement - We are not capturing enough user demographic data, usage metrics, and other KPIs. As a result, we do not engage the right users, we are not confident that our marketing is targeting the right users, and we are not selling enough premium Blast-Ups inside the games, which dramatically impacts our revenue. Mountkirk Games needs to create a repeatable and configurable mechanism for deploying isolated application environments. Developers and testers can access each other's environments and resources, but they cannot access staging or production resources. The staging environment needs access to some services from production. What should you do to isolate development environments from staging and production?
A. Create a project for development and test and another for staging and production
B. Create a network for development and test and another for staging and production
C. Create one subnetwork for development and another for staging and production
D. Create one project for development, a second for staging and a third for production
Your company is designing its application landscape on Compute Engine. Whenever a zonal outage occurs, the application should be restored in another zone as quickly as possible with the latest application data. You need to design the solution to meet this requirement. What should you do?
A. Create a snapshot schedule for the disk containing the application data. Whenever a zonal outage occurs, use the latest snapshot to restore the disk in the same zone.
B. Configure the Compute Engine instances with an instance template for the application, and use a regional persistent disk for the application data. Whenever a zonal outage occurs, use the instance template to spin up the application in another zone in the same region. Use the regional persistent disk for the application data.
C. Create a snapshot schedule for the disk containing the application data. Whenever a zonal outage occurs, use the latest snapshot to restore the disk in another zone within the same region.
D. Configure the Compute Engine instances with an instance template for the application, and use a regional persistent disk for the application data. Whenever a zonal outage occurs, use the instance template to spin up the application in another region. Use the regional persistent disk for the application data.
You are managing several internal applications that are deployed on Compute Engine. Business users inform you that an application has become very slow over the past few days. You want to find the underlying cause in order to solve the problem. What should you do first?
A. Inspect the logs and metrics from the instances in Cloud Logging and Cloud Monitoring.
B. Change the Compute Engine Instances behind the application to a machine type with more CPU and memory.
C. Restore a backup of the application database from a time before the application became slow.
D. Deploy the applications on a managed instance group with autoscaling enabled. Add a load balancer in front of the managed instance group, and have the users connect to the IP of the load balancer.
You are designing a Data Warehouse on Google Cloud and want to store sensitive data in BigQuery. Your company requires you to generate the encryption keys outside of Google Cloud. You need to implement a solution. What should you do?
A. Generate a new key in Cloud Key Management Service (Cloud KMS). Store all data in Cloud Storage using the customer-managed key option and select the created key. Set up a Dataflow pipeline to decrypt the data and to store it in a new BigQuery dataset.
B. Generate a new key in Cloud KMS. Create a dataset in BigQuery using the customer-managed key option and select the created key.
C. Import a key in Cloud KMS. Store all data in Cloud Storage using the customer-managed key option and select the created key. Set up a Dataflow pipeline to decrypt the data and to store it in a new BigQuery dataset.
D. Import a key in Cloud KMS. Create a dataset in BigQuery using the customer-supplied key option and select the created key.
Company overview - Mountkirk Games makes online, session-based, multiplayer games for mobile platforms. They have recently started expanding to other platforms after successfully migrating their on-premises environments to Google Cloud. Their most recent endeavor is to create a retro-style first-person shooter (FPS) game that allows hundreds of simultaneous players to join a geo-specific digital arena from multiple platforms and locations. A real-time digital banner will display a global leaderboard of all the top players across every active arena. Solution concept - Mountkirk Games is building a new multiplayer game that they expect to be very popular. They plan to deploy the game's backend on Google Kubernetes Engine so they can scale rapidly and use Google's global load balancer to route players to the closest regional game arenas. In order to keep the global leader board in sync, they plan to use a multi-region Spanner cluster. Existing technical environment - The existing environment was recently migrated to Google Cloud, and five games came across using lift-and-shift virtual machine migrations, with a few minor exceptions. Each new game exists in an isolated Google Cloud project nested below a folder that maintains most of the permissions and network policies. Legacy games with low traffic have been consolidated into a single project. There are also separate environments for development and testing. Business requirements - Support multiple gaming platforms. Support multiple regions. Support rapid iteration of game features. Minimize latency. Optimize for dynamic scaling. Use managed services and pooled resources. Minimize costs. Technical requirements - Dynamically scale based on game activity. Publish scoring data on a near real-time global leaderboard. Store game activity logs in structured files for future analysis. Use GPU processing to render graphics server-side for multi-platform support. Support eventual migration of legacy games to this new platform. Executive statement - Our last game was the first time we used Google Cloud, and it was a tremendous success. We were able to analyze player behavior and game telemetry in ways that we never could before. This success allowed us to bet on a full migration to the cloud and to start building all-new games using cloud-native design principles. Our new game is our most ambitious to date and will open up doors for us to support more gaming platforms beyond mobile. Latency is our top priority, although cost management is the next most important challenge. As with our first cloud-based game, we have grown to expect the cloud to enable advanced analytics capabilities so we can rapidly iterate on our deployments of bug fixes and new functionality. Mountkirk Games wants to limit the physical location of resources to their operating Google Cloud regions. What should you do?
A. Configure an organizational policy which constrains where resources can be deployed.
B. Configure IAM conditions to limit what resources can be configured.
C. Configure the quotas for resources in the regions not being used to 0.
D. Configure a custom alert in Cloud Monitoring so you can disable resources as they are created in other regions.
Company overview - EHR Healthcare is a leading provider of electronic health record software to the medical industry. EHR Healthcare provides their software as a service to multi- national medical offices, hospitals, and insurance providers. Solution concept - Due to rapid changes in the healthcare and insurance industry, EHR Healthcare's business has been growing exponentially year over year. They need to be able to scale their environment, adapt their disaster recovery plan, and roll out new continuous deployment capabilities to update their software at a fast pace. Google Cloud has been chosen to replace their current colocation facilities. Existing technical environment - EHR's software is currently hosted in multiple colocation facilities. The lease on one of the data centers is about to expire. Customer-facing applications are web-based, and many have recently been containerized to run on a group of Kubernetes clusters. Data is stored in a mixture of relational and NoSQL databases (MySQL, MS SQL Server, Redis, and MongoDB). EHR is hosting several legacy file- and API-based integrations with insurance providers on-premises. These systems are scheduled to be replaced over the next several years. There is no plan to upgrade or move these systems at the current time. Users are managed via Microsoft Active Directory. Monitoring is currently being done via various open source tools. Alerts are sent via email and are often ignored. Business requirements - * On-board new insurance providers as quickly as possible. * Provide a minimum 99.9% availability for all customer-facing systems. * Provide centralized visibility and proactive action on system performance and usage. * Increase ability to provide insights into healthcare trends. * Reduce latency to all customers. * Maintain regulatory compliance. * Decrease infrastructure administration costs. * Make predictions and generate reports on industry trends based on provider data. Technical requirements - * Maintain legacy interfaces to insurance providers with connectivity to both on-premises systems and cloud providers. * Provide a consistent way to manage customer-facing applications that are container-based. * Provide a secure and high-performance connection between on-premises systems and Google Cloud. * Provide consistent logging, log retention, monitoring, and alerting capabilities. * Maintain and manage multiple container-based environments. * Dynamically scale and provision new environments. * Create interfaces to ingest and process data from new providers. Executive statement - Our on-premises strategy has worked for years but has required a major investment of time and money in training our team on distinctly different systems, managing similar but separate environments, and responding to outages. Many of these outages have been a result of misconfigured systems, inadequate capacity to manage spikes in traffic, and inconsistent monitoring practices. We want to use Google Cloud to leverage a scalable, resilient platform that can span multiple environments seamlessly and provide a consistent and stable user experience that positions us for future growth. You need to upgrade the EHR connection to comply with their requirements. The new connection design must support business-critical needs and meet the same network and security policy requirements. What should you do?
A. Add a new Dedicated Interconnect connection.
B. Upgrade the bandwidth on the Dedicated Interconnect connection to 100 G.
C. Add three new Cloud VPN connections.
D. Add a new Carrier Peering connection.
Your company has an application running on Compute Engine that allows users to play their favorite music. There are a fixed number of instances. Files are stored in Cloud Storage, and data is streamed directly to users. Users are reporting that they sometimes need to attempt to play popular songs multiple times before they are successful. You need to improve the performance of the application. What should you do?
A. 1. Mount the Cloud Storage bucket using gcsfuse on all backend Compute Engine instances. 2. Serve music files directly from the backend Compute Engine instance.
B. 1. Create a Cloud Filestore NFS volume and attach it to the backend Compute Engine instances. 2. Download popular songs in Cloud Filestore. 3. Serve music files directly from the backend Compute Engine instance.
C. 1. Copy popular songs into CloudSQL as a blob. 2. Update application code to retrieve data from CloudSQL when Cloud Storage is overloaded.
D. 1. Create a managed instance group with Compute Engine instances. 2. Create a global load balancer and configure it with two backends: ג—‹ Managed instance group ג—‹ Cloud Storage bucket 3. Enable Cloud CDN on the bucket backend.
Company Overview - TerramEarth manufactures heavy equipment for the mining and agricultural industries. About 80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive. Solution Concept - There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second. Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced. The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules. Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second, with 22 hours of operation per day, TerramEarth collects a total of about 9 TB/day from these connected vehicles. Existing Technical Environment - TerramEarth's existing architecture is composed of Linux and Windows-based systems that reside in a single U.S, west coast based data center. These systems gzip CSV files from the field and upload via FTP, and place the data in their data warehouse. Because this process takes time, aggregated reports are based on data that is 3 weeks old. With this data, TerramEarth has been able to preemptively stock replacement parts and reduce unplanned downtime of their vehicles by 60%. However, because the data is stale, some customers are without their vehicles for up to 4 weeks while they wait for replacement parts. Business Requirements - Decrease unplanned vehicle downtime to less than 1 week Support the dealer network with more data on how their customers use their equipment to better position new products and services Have the ability to partner with different companies `" especially with seed and fertilizer suppliers in the fast-growing agricultural business `" to create compelling joint offerings for their customers Technical Requirements - Expand beyond a single datacenter to decrease latency to the American midwest and east coast Create a backup strategy Increase security of data transfer from equipment to the datacenter Improve data in the data warehouse Use customer and equipment data to anticipate customer needs Application 1: Data ingest - A custom Python application reads uploaded datafiles from a single server, writes to the data warehouse. Compute: Windows Server 2008 R2 - 16 CPUs - 128 GB of RAM - 10 TB local HDD storage Application 2: Reporting - An off the shelf application that business analysts use to run a daily report to see what equipment needs repair. Only 2 analysts of a team of 10 (5 west coast, 5 east coast) can connect to the reporting application at a time. Compute: Off the shelf application. License tied to number of physical CPUs - Windows Server 2008 R2 - 16 CPUs - 32 GB of RAM - 500 GB HDD Data warehouse: A single PostgreSQL server - RedHat Linux - 64 CPUs - 128 GB of RAM - 4x 6TB HDD in RAID 0 Executive Statement - Our competitive advantage has always been in our manufacturing process, with our ability to build better vehicles for lower cost than our competitors. However, new products with different approaches are constantly being developed, and I'm concerned that we lack the skills to undergo the next wave of transformations in our industry. My goals are to build our skills while addressing immediate market needs through incremental innovations. For this question, refer to the TerramEarth case study. You need to implement a reliable, scalable GCP solution for the data warehouse for your company, TerramEarth. Considering the TerramEarth business and technical requirements, what should you do?
A. Replace the existing data warehouse with BigQuery. Use table partitioning.
B. Replace the existing data warehouse with a Compute Engine instance with 96 CPUs.
C. Replace the existing data warehouse with BigQuery. Use federated data sources.
D. Replace the existing data warehouse with a Compute Engine instance with 96 CPUs. Add an additional Compute Engine preemptible instance with 32 CPUs.
You need to migrate Hadoop jobs for your company's Data Science team without modifying the underlying infrastructure. You want to minimize costs and infrastructure management effort. What should you do?
A. Create a Dataproc cluster using standard worker instances.
B. Create a Dataproc cluster using preemptible worker instances.
C. Manually deploy a Hadoop cluster on Compute Engine using standard instances.
D. Manually deploy a Hadoop cluster on Compute Engine using preemptible instances.
The operations manager asks you for a list of recommended practices that she should consider when migrating a J2EE application to the cloud. Which three practices should you recommend? (Choose three.)
A. Port the application code to run on Google App Engine
B. Integrate Cloud Dataflow into the application to capture real-time metrics
C. Instrument the application with a monitoring tool like Stackdriver Debugger
D. Select an automation framework to reliably provision the cloud infrastructure
E. Deploy a continuous integration tool with automated testing in a staging environment
F. Migrate from MySQL to a managed NoSQL database like Google Cloud Datastore or Bigtable
Company Overview - JencoMart is a global retailer with over 10,000 stores in 16 countries. The stores carry a range of goods, such as groceries, tires, and jewelry. One of the company's core values is excellent customer service. In addition, they recently introduced an environmental policy to reduce their carbon output by 50% over the next 5 years. Company Background - JencoMart started as a general store in 1931, and has grown into one of the world's leading brands, known for great value and customer service. Over time, the company transitioned from only physical stores to a stores and online hybrid model, with 25% of sales online. Currently, JencoMart has little presence in Asia, but considers that market key for future growth. Solution Concept - JencoMart wants to migrate several critical applications to the cloud but has not completed a technical review to determine their suitability for the cloud and the engineering required for migration. They currently host all of these applications on infrastructure that is at its end of life and is no longer supported. Existing Technical Environment - JencoMart hosts all of its applications in 4 data centers: 3 in North American and 1 in Europe; most applications are dual-homed. JencoMart understands the dependencies and resource usage metrics of their on-premises architecture. Application: Customer loyalty portal LAMP (Linux, Apache, MySQL and PHP) application served from the two JencoMart-owned U.S. data centers. Database - Oracle Database stores user profiles - 20 TB - Complex table structure - Well maintained, clean data - Strong backup strategy PostgreSQL database stores user credentials - Single-homed in US West - No redundancy - Backed up every 12 hours - 100% uptime service level agreement (SLA) - Authenticates all users Compute - 30 machines in US West Coast, each machine has: - Twin, dual core CPUs - 32 GB of RAM - Twin 250 GB HDD (RAID 1) 20 machines in US East Coast, each machine has: - Single, dual-core CPU - 24 GB of RAM - Twin 250 GB HDD (RAID 1) Storage - Access to shared 100 TB SAN in each location Tape backup every week Business Requirements - Optimize for capacity during peak periods and value during off-peak periods Guarantee service availability and support Reduce on-premises footprint and associated financial and environmental impact Move to outsourcing model to avoid large upfront costs associated with infrastructure purchase Expand services into Asia Technical Requirements - Assess key application for cloud suitability Modify applications for the cloud Move applications to a new infrastructure Leverage managed services wherever feasible Sunset 20% of capacity in existing data centers Decrease latency in Asia CEO Statement - JencoMart will continue to develop personal relationships with our customers as more people access the web. The future of our retail business is in the global market and the connection between online and in-store experiences. As a large, global company, we also have a responsibility to the environment through `green` initiatives and policies. CTO Statement - The challenges of operating data centers prevent focus on key technologies critical to our long-term success. Migrating our data services to a public cloud infrastructure will allow us to focus on big data and machine learning to improve our service to customers. CFO Statement - Since its founding, JencoMart has invested heavily in our data services infrastructure. However, because of changing market trends, we need to outsource our infrastructure to ensure our long-term success. This model will allow us to respond to increasing customer demand during peak periods and reduce costs. JencoMart has decided to migrate user profile storage to Google Cloud Datastore and the application servers to Google Compute Engine (GCE). During the migration, the existing infrastructure will need access to Datastore to upload the data. What service account key-management strategy should you recommend?
A. Provision service account keys for the on-premises infrastructure and for the GCE virtual machines (VMs)
B. Authenticate the on-premises infrastructure with a user account and provision service account keys for the VMs
C. Provision service account keys for the on-premises infrastructure and use Google Cloud Platform (GCP) managed keys for the VMs
D. Deploy a custom authentication service on GCE/Google Kubernetes Engine (GKE) for the on-premises infrastructure and use GCP managed keys for the VMs
Your company has a Google Cloud project that uses BigQuery for data warehousing on a pay-per-use basis. You want to monitor queries in real time to discover the most costly queries and which users spend the most. What should you do?
A. 1. In the BigQuery dataset that contains all the tables to be queried, add a label for each user that can launch a query. 2. Open the Billing page of the project. 3. Select Reports. 4. Select BigQuery as the product and filter by the user you want to check.
B. 1. Create a Cloud Logging sink to export BigQuery data access logs to BigQuery. 2. Perform a BigQuery query on the generated table to extract the information you need.
C. 1. Create a Cloud Logging sink to export BigQuery data access logs to Cloud Storage. 2. Develop a Dataflow pipeline to compute the cost of queries split by users.
D. 1. Activate billing export into BigQuery. 2. Perform a BigQuery query on the billing table to extract the information you need.
You have found an error in your App Engine application caused by missing Cloud Datastore indexes. You have created a YAML file with the required indexes and want to deploy these new indexes to Cloud Datastore. What should you do?
A. Point gcloud datastore create-indexes to your configuration file
B. Upload the configuration file to App Engine’s default Cloud Storage bucket, and have App Engine detect the new indexes
C. In the GCP Console, use Datastore Admin to delete the current indexes and upload the new configuration file
D. Create an HTTP request to the built-in python module to send the index configuration file to your application
You are deploying an application to Google Cloud. The application is part of a system. The application in Google Cloud must communicate over a private network with applications in a non-Google Cloud environment. The expected average throughput is 200 kbps. The business requires: ✑ as close to 100% system availability as possible ✑ cost optimization You need to design the connectivity between the locations to meet the business requirements. What should you provision?
A. An HA Cloud VPN gateway connected with two tunnels to an on-premises VPN gateway
B. Two Classic Cloud VPN gateways connected to two on-premises VPN gateways Configure each Classic Cloud VPN gateway to have two tunnels, each connected to different on-premises VPN gateways
C. Two HA Cloud VPN gateways connected to two on-premises VPN gateways Configure each HA Cloud VPN gateway to have two tunnels, each connected to different on-premises VPN gateways
D. A single Cloud VPN gateway connected to an on-premises VPN gateway
Your company is developing a web-based application. You need to make sure that production deployments are linked to source code commits and are fully auditable. What should you do?
A. Make sure a developer is tagging the code commit with the date and time of commit.
B. Make sure a developer is adding a comment to the commit that links to the deployment.
C. Make the container tag match the source code commit hash.
D. Make sure the developer is tagging the commits with latest.
Company overview - TerramEarth manufactures heavy equipment for the mining and agricultural industries. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive. Solution concept - There are 2 million TerramEarth vehicles in operation currently, and we see 20% yearly growth. Vehicles collect telemetry data from many sensors during operation. A small subset of critical data is transmitted from the vehicles in real time to facilitate fleet management. The rest of the sensor data is collected, compressed, and uploaded daily when the vehicles return to home base. Each vehicle usually generates 200 to 500 megabytes of data per day. Existing technical environment - TerramEarth's vehicle data aggregation and analysis infrastructure resides in Google Cloud and serves clients from all around the world. A growing amount of sensor data is captured from their two main manufacturing plants and sent to private data centers that contain their legacy inventory and logistics management systems. The private data centers have multiple network interconnects configured to Google Cloud. The web frontend for dealers and customers is running in Google Cloud and allows access to stock management and analytics. Business requirements - * Predict and detect vehicle malfunction and rapidly ship parts to dealerships for just-in-time repair where possible. * Decrease cloud operational costs and adapt to seasonality. * Increase speed and reliability of development workflow. * Allow remote developers to be productive without compromising code or data security. * Create a flexible and scalable platform for developers to create custom API services for dealers and partners. Technical requirements - * Create a new abstraction layer for HTTP API access to their legacy systems to enable a gradual move into the cloud without disrupting operations. * Modernize all CI/CD pipelines to allow developers to deploy container-based workloads in highly scalable environments. * Allow developers to run experiments without compromising security and governance requirements. * Create a self-service portal for internal and partner developers to create new projects, request resources for data analytics jobs, and centrally manage access to the API endpoints. * Use cloud-native solutions for keys and secrets management and optimize for identity-based access. * Improve and standardize tools necessary for application and network monitoring and troubleshooting. Executive statement - Our competitive advantage has always been our focus on the customer, with our ability to provide excellent customer service and minimize vehicle downtimes. After moving multiple systems into Google Cloud, we are seeking new ways to provide best-in-class online fleet management services to our customers and improve operations of our dealerships. Our 5-year strategic plan is to create a partner ecosystem of new products by enabling access to our data, increasing autonomous operation capabilities of our vehicles, and creating a path to move the remaining legacy systems to the cloud. For this question, refer to the TerramEarth case study. TerramEarth has a legacy web application that you cannot migrate to cloud. However, you still want to build a cloud-native way to monitor the application. If the application goes down, you want the URL to point to a "Site is unavailable" page as soon as possible. You also want your Ops team to receive a notification for the issue. You need to build a reliable solution for minimum cost. What should you do?
A. Create a scheduled job in Cloud Run to invoke a container every minute. The container will check the application URL. If the application is down, switch the URL to the “Site is unavailable” page, and notify the Ops team.
B. Create a cron job on a Compute Engine VM that runs every minute. The cron job invokes a Python program to check the application URL. If the application is down, switch the URL to the “Site is unavailable” page, and notify the Ops team.
C. Create a Cloud Monitoring uptime check to validate the application URL. If it fails, put a message in a Pub/Sub queue that triggers a Cloud Function to switch the URL to the “Site is unavailable” page, and notify the Ops team.
D. Use Cloud Error Reporting to check the application URL. If the application is down, switch the URL to the “Site is unavailable” page, and notify the Ops team.
Company overview - TerramEarth manufactures heavy equipment for the mining and agricultural industries. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive. Solution concept - There are 2 million TerramEarth vehicles in operation currently, and we see 20% yearly growth. Vehicles collect telemetry data from many sensors during operation. A small subset of critical data is transmitted from the vehicles in real time to facilitate fleet management. The rest of the sensor data is collected, compressed, and uploaded daily when the vehicles return to home base. Each vehicle usually generates 200 to 500 megabytes of data per day. Existing technical environment - TerramEarth's vehicle data aggregation and analysis infrastructure resides in Google Cloud and serves clients from all around the world. A growing amount of sensor data is captured from their two main manufacturing plants and sent to private data centers that contain their legacy inventory and logistics management systems. The private data centers have multiple network interconnects configured to Google Cloud. The web frontend for dealers and customers is running in Google Cloud and allows access to stock management and analytics. Business requirements - * Predict and detect vehicle malfunction and rapidly ship parts to dealerships for just-in-time repair where possible. * Decrease cloud operational costs and adapt to seasonality. * Increase speed and reliability of development workflow. * Allow remote developers to be productive without compromising code or data security. * Create a flexible and scalable platform for developers to create custom API services for dealers and partners. Technical requirements - * Create a new abstraction layer for HTTP API access to their legacy systems to enable a gradual move into the cloud without disrupting operations. * Modernize all CI/CD pipelines to allow developers to deploy container-based workloads in highly scalable environments. * Allow developers to run experiments without compromising security and governance requirements. * Create a self-service portal for internal and partner developers to create new projects, request resources for data analytics jobs, and centrally manage access to the API endpoints. * Use cloud-native solutions for keys and secrets management and optimize for identity-based access. * Improve and standardize tools necessary for application and network monitoring and troubleshooting. Executive statement - Our competitive advantage has always been our focus on the customer, with our ability to provide excellent customer service and minimize vehicle downtimes. After moving multiple systems into Google Cloud, we are seeking new ways to provide best-in-class online fleet management services to our customers and improve operations of our dealerships. Our 5-year strategic plan is to create a partner ecosystem of new products by enabling access to our data, increasing autonomous operation capabilities of our vehicles, and creating a path to move the remaining legacy systems to the cloud. For this question, refer to the TerramEarth case study. You start to build a new application that uses a few Cloud Functions for the backend. One use case requires a Cloud Function func_display to invoke another Cloud Function func_query. You want func_query only to accept invocations from func_display. You also want to follow Google's recommended best practices. What should you do?
A. Create a token and pass it in as an environment variable to func_display. When invoking func_query, include the token in the request. Pass the same token to func_query and reject the invocation if the tokens are different.
B. Make func_query ‘Require authentication.’ Create a unique service account and associate it to func_display. Grant the service account invoker role for func_query. Create an id token in func_display and include the token to the request when invoking func_query.
C. Make func_query ‘Require authentication’ and only accept internal traffic. Create those two functions in the same VPC. Create an ingress firewall rule for func_query to only allow traffic from func_display.
D. Create those two functions in the same project and VPC. Make func_query only accept internal traffic. Create an ingress firewall for func_query to only allow traffic from func_display. Also, make sure both functions use the same service account.
Company Overview - Dress4Win is a web-based company that helps their users organize and manage their personal wardrobe using a website and mobile application. The company also cultivates an active social network that connects their users with designers and retailers. They monetize their services through advertising, e-commerce, referrals, and a premium app model. Company Background - Dress4Win's application has grown from a few servers in the founder's garage to several hundred servers and appliances in a collocated data center. However, the capacity of their infrastructure is now insufficient for the application's rapid growth. Because of this growth and the company's desire to innovate faster, Dress4Win is committing to a full migration to a public cloud. Solution Concept - For the first phase of their migration to the cloud, Dress4Win is considering moving their development and test environments. They are also considering building a disaster recovery site, because their current infrastructure is at a single location. They are not sure which components of their architecture they can migrate as is and which components they need to change before migrating them. Existing Technical Environment - The Dress4Win application is served out of a single data center location. Databases: - MySQL - user data, inventory, static data - Redis - metadata, social graph, caching Application servers: - Tomcat - Java micro-services - Nginx - static content - Apache Beam - Batch processing Storage appliances: - iSCSI for VM hosts - Fiber channel SAN - MySQL databases - NAS - image storage, logs, backups Apache Hadoop/Spark servers: - Data analysis - Real-time trending calculations MQ servers: - Messaging - Social notifications - Events Miscellaneous servers: - Jenkins, monitoring, bastion hosts, security scanners Business Requirements - Build a reliable and reproducible environment with scaled parity of production.
A. Create a dump of the on-premises MySQL master server, and then shut it down, upload it to the cloud environment, and load into a new MySQL cluster.
B. Setup a MySQL replica server/slave in the cloud environment, and configure it for asynchronous replication from the MySQL master server on-premises until cutover.
C. Create a new MySQL cluster in the cloud, configure applications to begin writing to both on premises and cloud MySQL masters, and destroy the original cluster at cutover.
D. Create a dump of the MySQL replica server into the cloud environment, load it into: Google Cloud Datastore, and configure applications to read/write to Cloud Datastore at cutover.
Your company has decided to make a major revision of their API in order to create better experiences for their developers. They need to keep the old version of the API available and deployable, while allowing new customers and testers to try out the new API. They want to keep the same SSL and DNS records in place to serve both APIs. What should they do?
A. Configure a new load balancer for the new version of the API
B. Reconfigure old clients to use a new endpoint for the new API
C. Have the old API forward traffic to the new API based on the path
D. Use separate backend pools for each API path behind the load balancer
Company Overview - TerramEarth manufactures heavy equipment for the mining and agricultural industries: about 80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive. Company background - TerramEarth was formed in 1946, when several small, family owned companies combined to retool after World War II. The company cares about their employees and customers and considers them to be extended members of their family. TerramEarth is proud of their ability to innovate on their core products and find new markets as their customers' needs change. For the past 20 years, trends in the industry have been largely toward increasing productivity by using larger vehicles with a human operator. Solution Concept - There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second. Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced. The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules. Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second with 22 hours of operation per day, Terram Earth collects a total of about 9 TB/day from these connected vehicles. Existing Technical Environment -
A. Migrate from CSV to binary format, migrate from FTP to SFTP transport, and develop machine learning analysis of metrics
B. Migrate from FTP to streaming transport, migrate from CSV to binary format, and develop machine learning analysis of metrics
C. Increase fleet cellular connectivity to 80%, migrate from FTP to streaming transport, and develop machine learning analysis of metrics
D. Migrate from FTP to SFTP transport, develop machine learning analysis of metrics, and increase dealer local inventory by a fixed factor
Company overview - TerramEarth manufactures heavy equipment for the mining and agricultural industries. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive. Solution concept - There are 2 million TerramEarth vehicles in operation currently, and we see 20% yearly growth. Vehicles collect telemetry data from many sensors during operation. A small subset of critical data is transmitted from the vehicles in real time to facilitate fleet management. The rest of the sensor data is collected, compressed, and uploaded daily when the vehicles return to home base. Each vehicle usually generates 200 to 500 megabytes of data per day. Existing technical environment - TerramEarth's vehicle data aggregation and analysis infrastructure resides in Google Cloud and serves clients from all around the world. A growing amount of sensor data is captured from their two main manufacturing plants and sent to private data centers that contain their legacy inventory and logistics management systems. The private data centers have multiple network interconnects configured to Google Cloud. The web frontend for dealers and customers is running in Google Cloud and allows access to stock management and analytics. Business requirements - * Predict and detect vehicle malfunction and rapidly ship parts to dealerships for just-in-time repair where possible. * Decrease cloud operational costs and adapt to seasonality. * Increase speed and reliability of development workflow. * Allow remote developers to be productive without compromising code or data security. * Create a flexible and scalable platform for developers to create custom API services for dealers and partners. Technical requirements - * Create a new abstraction layer for HTTP API access to their legacy systems to enable a gradual move into the cloud without disrupting operations. * Modernize all CI/CD pipelines to allow developers to deploy container-based workloads in highly scalable environments. * Allow developers to run experiments without compromising security and governance requirements. * Create a self-service portal for internal and partner developers to create new projects, request resources for data analytics jobs, and centrally manage access to the API endpoints. * Use cloud-native solutions for keys and secrets management and optimize for identity-based access. * Improve and standardize tools necessary for application and network monitoring and troubleshooting. Executive statement - Our competitive advantage has always been our focus on the customer, with our ability to provide excellent customer service and minimize vehicle downtimes. After moving multiple systems into Google Cloud, we are seeking new ways to provide best-in-class online fleet management services to our customers and improve operations of our dealerships. Our 5-year strategic plan is to create a partner ecosystem of new products by enabling access to our data, increasing autonomous operation capabilities of our vehicles, and creating a path to move the remaining legacy systems to the cloud. For this question, refer to the TerramEarth case study. TerramEarth has about 1 petabyte (PB) of vehicle testing data in a private data center. You want to move the data to Cloud Storage for your machine learning team. Currently, a 1-Gbps interconnect link is available for you. The machine learning team wants to start using the data in a month. What should you do?
A. Request Transfer Appliances from Google Cloud, export the data to appliances, and return the appliances to Google Cloud.
B. Configure the Storage Transfer service from Google Cloud to send the data from your data center to Cloud Storage.
C. Make sure there are no other users consuming the 1Gbps link, and use multi-thread transfer to upload the data to Cloud Storage.
D. Export files to an encrypted USB device, send the device to Google Cloud, and request an import of the data to Cloud Storage.
Company Overview - TerramEarth manufactures heavy equipment for the mining and agricultural industries: about 80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive. Company background - TerramEarth was formed in 1946, when several small, family owned companies combined to retool after World War II. The company cares about their employees and customers and considers them to be extended members of their family. TerramEarth is proud of their ability to innovate on their core products and find new markets as their customers' needs change. For the past 20 years, trends in the industry have been largely toward increasing productivity by using larger vehicles with a human operator. Solution Concept - There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second. Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced. The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules. Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second with 22 hours of operation per day, Terram Earth collects a total of about 9 TB/day from these connected vehicles. Existing Technical Environment -
A. Opex/capex allocation, LAN changes, capacity planning
B. Capacity planning, TCO calculations, opex/capex allocation
C. Capacity planning, utilization measurement, data center expansion
D. Data Center expansion, TCO calculations, utilization measurement
Company Overview - Mountkirk Games makes online, session-based, multiplayer games for mobile platforms. They build all of their games using some server-side integration. Historically, they have used cloud providers to lease physical servers. Due to the unexpected popularity of some of their games, they have had problems scaling their global audience, application servers, MySQL databases, and analytics tools. Their current model is to write game statistics to files and send them through an ETL tool that loads them into a centralized MySQL database for reporting. Solution Concept - Mountkirk Games is building a new game, which they expect to be very popular. They plan to deploy the game's backend on Google Compute Engine so they can capture streaming metrics, run intensive analytics, and take advantage of its autoscaling server environment and integrate with a managed NoSQL database. Business Requirements - Increase to a global footprint Improve uptime `" downtime is loss of players Increase efficiency of the cloud resources we use Reduce latency to all customers Technical Requirements - Requirements for Game Backend Platform Dynamically scale up or down based on game activity Connect to a transactional database service to manage user profiles and game state Store game activity in a timeseries database service for future analysis As the system scales, ensure that data is not lost due to processing backlogs Run hardened Linux distro Requirements for Game Analytics Platform Dynamically scale up or down based on game activity Process incoming data on the fly directly from the game servers Process data that arrives late because of slow mobile networks Allow queries to access at least 10 TB of historical data Process files that are regularly uploaded by users' mobile devices Executive Statement - Our last successful game did not scale well with our previous cloud provider, resulting in lower user adoption and affecting the game's reputation. Our investors want more key performance indicators (KPIs) to evaluate the speed and stability of the game, as well as other metrics that provide deeper insight into usage patterns so we can adapt the game to target users. Additionally, our current technology stack cannot provide the scale we need, so we want to replace MySQL and move to an environment that provides autoscaling, low latency load balancing, and frees us up from managing physical servers. For this question, refer to the Mountkirk Games case study. Which managed storage option meets Mountkirk's technical requirement for storing game activity in a time series database service?
A. Cloud Bigtable
B. Cloud Spanner
C. BigQuery
D. Cloud Datastore
You are working in a highly secured environment where public Internet access from the Compute Engine VMs is not allowed. You do not yet have a VPN connection to access an on-premises file server. You need to install specific software on a Compute Engine instance. How should you install the software?
A. Upload the required installation files to Cloud Storage. Configure the VM on a subnet with a Private Google Access subnet. Assign only an internal IP address to the VM. Download the installation files to the VM using gsutil.
B. Upload the required installation files to Cloud Storage and use firewall rules to block all traffic except the IP address range for Cloud Storage. Download the files to the VM using gsutil.
C. Upload the required installation files to Cloud Source Repositories. Configure the VM on a subnet with a Private Google Access subnet. Assign only an internal IP address to the VM. Download the installation files to the VM using gcloud.
D. Upload the required installation files to Cloud Source Repositories and use firewall rules to block all traffic except the IP address range for Cloud Source Repositories. Download the files to the VM using gsutil.
You have been engaged by your client to lead the migration of their application infrastructure to GCP. One of their current problems is that the on-premises high performance SAN is requiring frequent and expensive upgrades to keep up with the variety of workloads that are identified as follows: 20 TB of log archives retained for legal reasons; 500 GB of VM boot/data volumes and templates; 500 GB of image thumbnails; 200 GB of customer session state data that allows customers to restart sessions even if off-line for several days. Which of the following best reflects your recommendations for a cost-effective storage allocation?
A. Local SSD for customer session state data. Lifecycle-managed Cloud Storage for log archives, thumbnails, and VM boot/data volumes.
B. Memcache backed by Cloud Datastore for the customer session state data. Lifecycle-managed Cloud Storage for log archives, thumbnails, and VM boot/data volumes.
C. Memcache backed by Cloud SQL for customer session state data. Assorted local SSD-backed instances for VM boot/data volumes. Cloud Storage for log archives and thumbnails.
D. Memcache backed by Persistent Disk SSD storage for customer session state data. Assorted local SSD-backed instances for VM boot/data volumes. Cloud Storage for log archives and thumbnails.
You want to create a private connection between your instances on Compute Engine and your on-premises data center. You require a connection of at least 20 Gbps. You want to follow Google-recommended practices. How should you set up the connection?
A. Create a VPC and connect it to your on-premises data center using Dedicated Interconnect.
B. Create a VPC and connect it to your on-premises data center using a single Cloud VPN.
C. Create a Cloud Content Delivery Network (Cloud CDN) and connect it to your on-premises data center using Dedicated Interconnect.
D. Create a Cloud Content Delivery Network (Cloud CDN) and connect it to your on-premises datacenter using a single Cloud VPN.
Your company is using BigQuery as its enterprise data warehouse. Data is distributed over several Google Cloud projects. All queries on BigQuery need to be billed on a single project. You want to make sure that no query costs are incurred on the projects that contain the data. Users should be able to query the datasets, but not edit them. How should you configure users' access roles?
A. Add all users to a group. Grant the group the role of BigQuery user on the billing project and BigQuery dataViewer on the projects that contain the data.
B. Add all users to a group. Grant the group the roles of BigQuery dataViewer on the billing project and BigQuery user on the projects that contain the data.
C. Add all users to a group. Grant the group the roles of BigQuery jobUser on the billing project and BigQuery dataViewer on the projects that contain the data.
D. Add all users to a group. Grant the group the roles of BigQuery dataViewer on the billing project and BigQuery jobUser on the projects that contain the data.
Company overview - Mountkirk Games makes online, session-based, multiplayer games for mobile platforms. They have recently started expanding to other platforms after successfully migrating their on-premises environments to Google Cloud. Their most recent endeavor is to create a retro-style first-person shooter (FPS) game that allows hundreds of simultaneous players to join a geo-specific digital arena from multiple platforms and locations. A real-time digital banner will display a global leaderboard of all the top players across every active arena. Solution concept - Mountkirk Games is building a new multiplayer game that they expect to be very popular. They plan to deploy the game's backend on Google Kubernetes Engine so they can scale rapidly and use Google's global load balancer to route players to the closest regional game arenas. In order to keep the global leader board in sync, they plan to use a multi-region Spanner cluster. Existing technical environment - The existing environment was recently migrated to Google Cloud, and five games came across using lift-and-shift virtual machine migrations, with a few minor exceptions. Each new game exists in an isolated Google Cloud project nested below a folder that maintains most of the permissions and network policies. Legacy games with low traffic have been consolidated into a single project. There are also separate environments for development and testing. Business requirements - Support multiple gaming platforms. Support multiple regions. Support rapid iteration of game features. Minimize latency. Optimize for dynamic scaling. Use managed services and pooled resources. Minimize costs. Technical requirements - Dynamically scale based on game activity. Publish scoring data on a near real-time global leaderboard. Store game activity logs in structured files for future analysis. Use GPU processing to render graphics server-side for multi-platform support. Support eventual migration of legacy games to this new platform. Executive statement - Our last game was the first time we used Google Cloud, and it was a tremendous success. We were able to analyze player behavior and game telemetry in ways that we never could before. This success allowed us to bet on a full migration to the cloud and to start building all-new games using cloud-native design principles. Our new game is our most ambitious to date and will open up doors for us to support more gaming platforms beyond mobile. Latency is our top priority, although cost management is the next most important challenge. As with our first cloud-based game, we have grown to expect the cloud to enable advanced analytics capabilities so we can rapidly iterate on our deployments of bug fixes and new functionality. You need to implement a network ingress for a new game that meets the defined business and technical requirements. Mountkirk Games wants each regional game instance to be located in multiple Google Cloud regions. What should you do?
A. Configure a global load balancer connected to a managed instance group running Compute Engine instances.
B. Configure kubemci with a global load balancer and Google Kubernetes Engine.
C. Configure a global load balancer with Google Kubernetes Engine.
D. Configure Ingress for Anthos with a global load balancer and Google Kubernetes Engine.
Company overview - Helicopter Racing League (HRL) is a global sports league for competitive helicopter racing. Each year HRL holds the world championship and several regional league competitions where teams compete to earn a spot in the world championship. HRL offers a paid service to stream the races all over the world with live telemetry and predictions throughout each race. Solution concept - HRL wants to migrate their existing service to a new platform to expand their use of managed AI and ML services to facilitate race predictions. Additionally, as new fans engage with the sport, particularly in emerging regions, they want to move the serving of their content, both real-time and recorded, closer to their users. Existing technical environment - HRL is a public cloud-first company; the core of their mission-critical applications runs on their current public cloud provider. Video recording and editing is performed at the race tracks, and the content is encoded and transcoded, where needed, in the cloud. Enterprise-grade connectivity and local compute is provided by truck-mounted mobile data centers. Their race prediction services are hosted exclusively on their existing public cloud provider. Their existing technical environment is as follows: Existing content is stored in an object storage service on their existing public cloud provider. Video encoding and transcoding is performed on VMs created for each job. Race predictions are performed using TensorFlow running on VMs in the current public cloud provider. Business requirements - HRL's owners want to expand their predictive capabilities and reduce latency for their viewers in emerging markets. Their requirements are: Support ability to expose the predictive models to partners. Increase predictive capabilities during and before races: ג—‹ Race results ג—‹ Mechanical failures ג—‹ Crowd sentiment Increase telemetry and create additional insights. Measure fan engagement with new predictions. Enhance global availability and quality of the broadcasts. Increase the number of concurrent viewers. Minimize operational complexity. Ensure compliance with regulations. Create a merchandising revenue stream. Technical requirements - Maintain or increase prediction throughput and accuracy. Reduce viewer latency. Increase transcoding performance. Create real-time analytics of viewer consumption patterns and engagement. Create a data mart to enable processing of large volumes of race data. Executive statement - Our CEO, S. Hawke, wants to bring high-adrenaline racing to fans all around the world. We listen to our fans, and they want enhanced video streams that include predictions of events within the race (e.g., overtaking). Our current platform allows us to predict race outcomes but lacks the facility to support real-time predictions during races and the capacity to process season-long results. For this question, refer to the Helicopter Racing League (HRL) case study. Recently HRL started a new regional racing league in Cape Town, South Africa. In an effort to give customers in Cape Town a better user experience, HRL has partnered with the Content Delivery Network provider, Fastly. HRL needs to allow traffic coming from all of the Fastly IP address ranges into their Virtual Private Cloud network (VPC network). You are a member of the HRL security team and you need to configure the update that will allow only the Fastly IP address ranges through the External HTTP(S) load balancer. Which command should you use? A.B.
C.
D.
Your company is planning to migrate their Windows Server 2022 from their on-premises data center to Google Cloud. You need to bring the licenses that are currently in use in on-premises virtual machines into the target cloud environment. What should you do?
A. 1. Create an image of the on-premises virtual machines and upload into Cloud Storage.2. Import the image as a virtual disk on Compute Engine.
B. 1. Create standard instances on Compute Engine.2. Select as the OS the same Microsoft Windows version that is currently in use in the on-premises environment.
C. 1. Create an image of the on-premises virtual machine.2. Import the image as a virtual disk on Compute Engine.3. Create a standard instance on Compute Engine, selecting as the OS the same Microsoft Windows version that is currently in use in the on-premises environment.4. Attach a data disk that includes data that matches the created image.
D. 1. Create an image of the on-premises virtual machines.2. Import the image as a virtual disk on Compute Engine using –os=windows-2022-dc-v.3. Create a sole-tenancy instance on Compute Engine that uses the imported disk as a boot disk.
Your company has sensitive data in Cloud Storage buckets. Data analysts have Identity Access Management (IAM) permissions to read the buckets. You want to prevent data analysts from retrieving the data in the buckets from outside the office network. What should you do?
A. 1. Create a VPC Service Controls perimeter that includes the projects with the buckets. 2. Create an access level with the CIDR of the office network.
B. 1. Create a firewall rule for all instances in the Virtual Private Cloud (VPC) network for source range. 2. Use the Classless Inter-domain Routing (CIDR) of the office network.
C. 1. Create a Cloud Function to remove IAM permissions from the buckets, and another Cloud Function to add IAM permissions to the buckets. 2. Schedule the Cloud Functions with Cloud Scheduler to add permissions at the start of business and remove permissions at the end of business.
D. 1. Create a Cloud VPN to the office network. 2. Configure Private Google Access for on-premises hosts.
Company Overview - TerramEarth manufactures heavy equipment for the mining and agricultural industries: about 80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive. Company background - TerramEarth was formed in 1946, when several small, family owned companies combined to retool after World War II. The company cares about their employees and customers and considers them to be extended members of their family. TerramEarth is proud of their ability to innovate on their core products and find new markets as their customers' needs change. For the past 20 years, trends in the industry have been largely toward increasing productivity by using larger vehicles with a human operator. Solution Concept - There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second. Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced. The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules. Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second with 22 hours of operation per day, Terram Earth collects a total of about 9 TB/day from these connected vehicles. Existing Technical Environment -B.
C.
D.
Company Overview - TerramEarth manufactures heavy equipment for the mining and agricultural industries: about 80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive. Company background - TerramEarth was formed in 1946, when several small, family owned companies combined to retool after World War II. The company cares about their employees and customers and considers them to be extended members of their family. TerramEarth is proud of their ability to innovate on their core products and find new markets as their customers' needs change. For the past 20 years, trends in the industry have been largely toward increasing productivity by using larger vehicles with a human operator. Solution Concept - There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second. Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced. The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules. Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second with 22 hours of operation per day, Terram Earth collects a total of about 9 TB/day from these connected vehicles. Existing Technical Environment -
A. Use Google App Engine with Google Cloud Endpoints. Focus on an API for dealers and partners
B. Use Google App Engine with a JAX-RS Jersey Java-based framework. Focus on an API for the public
C. Use Google App Engine with the Swagger (Open API Specification) framework. Focus on an API for the public
D. Use Google Container Engine with a Django Python container. Focus on an API for the public
E. Use Google Container Engine with a Tomcat container with the Swagger (Open API Specification) framework. Focus on an API for dealers and partners
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