Google Professional Cloud Architect Practice Test Free – 50 Real Exam Questions to Boost Your Confidence
Preparing for the Google Professional Cloud Architect exam? Start with our Google Professional Cloud Architect Practice Test Free – a set of 50 high-quality, exam-style questions crafted to help you assess your knowledge and improve your chances of passing on the first try.
Taking a Google Professional Cloud Architect practice test free is one of the smartest ways to:
- Get familiar with the real exam format and question types
- Evaluate your strengths and spot knowledge gaps
- Gain the confidence you need to succeed on exam day
Below, you will find 50 free Google Professional Cloud Architect practice questions to help you prepare for the exam. These questions are designed to reflect the real exam structure and difficulty level. You can click on each Question to explore the details.
You have developed an application using Cloud ML Engine that recognizes famous paintings from uploaded images. You want to test the application and allow specific people to upload images for the next 24 hours. Not all users have a Google Account. How should you have users upload images?
A. Have users upload the images to Cloud Storage. Protect the bucket with a password that expires after 24 hours.
B. Have users upload the images to Cloud Storage using a signed URL that expires after 24 hours.
C. Create an App Engine web application where users can upload images. Configure App Engine to disable the application after 24 hours. Authenticate users via Cloud Identity.
D. Create an App Engine web application where users can upload images for the next 24 hours. Authenticate users via Cloud Identity.
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.
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.
You are migrating your on-premises solution to Google Cloud in several phases. You will use Cloud VPN to maintain a connection between your on-premises systems and Google Cloud until the migration is completed. You want to make sure all your on-premise systems remain reachable during this period. How should you organize your networking in Google Cloud?
A. Use the same IP range on Google Cloud as you use on-premises
B. Use the same IP range on Google Cloud as you use on-premises for your primary IP range and use a secondary range that does not overlap with the range you use on-premises
C. Use an IP range on Google Cloud that does not overlap with the range you use on-premises
D. Use an IP range on Google Cloud that does not overlap with the range you use on-premises for your primary IP range and use a secondary range with the same IP range as you use on-premises
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.
Company Overview - Dress4Win is a web-based company that helps their users organize and manage their personal wardrobe using a web app 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 freemium app model. The application has grown from a few servers in the founder's garage to several hundred servers and appliances in a colocated 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 moving their development and test environments. They are also 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. All servers run Ubuntu LTS v16.04. Databases: MySQL. 1 server for user data, inventory, static data: - MySQL 5.8 - 8 core CPUs - 128 GB of RAM - 2x 5 TB HDD (RAID 1) Redis 3 server cluster for metadata, social graph, caching. Each server is: - Redis 3.2 - 4 core CPUs - 32GB of RAM Compute: 40 Web Application servers providing micro-services based APIs and static content. `" - Tomcat Java - - Nginx - 4 core CPUs - 32 GB of RAM 20 Apache Hadoop/Spark servers: - Data analysis - Real-time trending calculations - 8 core CPUs - 128 GB of RAM - 4x 5 TB HDD (RAID 1) 3 RabbitMQ servers for messaging, social notifications, and events: - 8 core CPUs - 32GB of RAM Miscellaneous servers: - Jenkins, monitoring, bastion hosts, security scanners - 8 core CPUs - 32GB of RAM Storage appliances: iSCSI for VM hosts Fiber channel SAN `" MySQL databases - 1 PB total storage; 400 TB available NAS `" image storage, logs, backups - 100 TB total storage; 35 TB available 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. Technical Requirements - Easily create non-production environments in the cloud. Implement an automation framework for provisioning resources in cloud. Implement a continuous deployment process for deploying applications to the on-premises datacenter or cloud. Support failover of the production environment to cloud during an emergency. Encrypt data on the wire and at rest. Support multiple private connections between the production data center and cloud environment. Executive Statement - Our investors are concerned about our ability to scale and contain costs with our current infrastructure. They are also concerned that a competitor could use a public cloud platform to offset their up-front investment and free them to focus on developing better features. Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80% of our capacity is sitting idle. 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 for a public cloud strategy achieves a cost reduction between 30% and 50% over our current model. For this question, refer to the Dress4Win case study. To be legally compliant during an audit, Dress4Win must be able to give insights in all administrative actions that modify the configuration or metadata of resources on Google Cloud. What should you do?
A. Use Stackdriver Trace to create a Trace list analysis.
B. Use Stackdriver Monitoring to create a dashboard on the project’s activity.
C. Enable Cloud Identity-Aware Proxy in all projects, and add the group of Administrators as a member.
D. Use the Activity page in the GCP Console and Stackdriver Logging to provide the required insight.
A production database virtual machine on Google Compute Engine has an ext4-formatted persistent disk for data files. The database is about to run out of storage space. How can you remediate the problem with the least amount of downtime?
A. In the Cloud Platform Console, increase the size of the persistent disk and use the resize2fs command in Linux.
B. Shut down the virtual machine, use the Cloud Platform Console to increase the persistent disk size, then restart the virtual machine
C. In the Cloud Platform Console, increase the size of the persistent disk and verify the new space is ready to use with the fdisk command in Linux
D. In the Cloud Platform Console, create a new persistent disk attached to the virtual machine, format and mount it, and configure the database service to move the files to the new disk
E. In the Cloud Platform Console, create a snapshot of the persistent disk restore the snapshot to a new larger disk, unmount the old disk, mount the new disk and restart the database service
Your company is running its application workloads on Compute Engine. The applications have been deployed in production, acceptance, and development environments. The production environment is business-critical and is used 24/7, while the acceptance and development environments are only critical during office hours. Your CFO has asked you to optimize these environments to achieve cost savings during idle times. What should you do?
A. Create a shell script that uses the gcloud command to change the machine type of the development and acceptance instances to a smaller machine type outside of office hours. Schedule the shell script on one of the production instances to automate the task.
B. Use Cloud Scheduler to trigger a Cloud Function that will stop the development and acceptance environments after office hours and start them just before office hours.
C. Deploy the development and acceptance applications on a managed instance group and enable autoscaling.
D. Use regular Compute Engine instances for the production environment, and use preemptible VMs for the acceptance and development environments.
You need to evaluate your team readiness for a new GCP project. You must perform the evaluation and create a skills gap plan which incorporates the business goal of cost optimization. Your team has deployed two GCP projects successfully to date. What should you do?
A. Allocate budget for team training. Set a deadline for the new GCP project.
B. Allocate budget for team training. Create a roadmap for your team to achieve Google Cloud certification based on job role.
C. Allocate budget to hire skilled external consultants. Set a deadline for the new GCP project.
D. Allocate budget to hire skilled external consultants. Create a roadmap for your team to achieve Google Cloud certification based on job role.
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 are building a microservice-based application for TerramEarth. The application is based on Docker containers. You want to follow Google-recommended practices to build the application continuously and store the build artifacts. What should you do?
A. Configure a trigger in Cloud Build for new source changes. Invoke Cloud Build to build container images for each microservice, and tag them using the code commit hash. Push the images to the Container Registry.
B. Configure a trigger in Cloud Build for new source changes. The trigger invokes build jobs and build container images for the microservices. Tag the images with a version number, and push them to Cloud Storage.
C. Create a Scheduler job to check the repo every minute. For any new change, invoke Cloud Build to build container images for the microservices. Tag the images using the current timestamp, and push them to the Container Registry.
D. Configure a trigger in Cloud Build for new source changes. Invoke Cloud Build to build one container image, and tag the image with the label ‘latest.’ Push the image to the Container Registry.
You are designing a large distributed application with 30 microservices. Each of your distributed microservices needs to connect to a database back-end. You want to store the credentials securely. Where should you store the credentials?
A. In the source code
B. In an environment variable
C. In a secret management system
D. In a config file that has restricted access through ACLs
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. TerramEarth has decided to store data files in Cloud Storage. You need to configure Cloud Storage lifecycle rule to store 1 year of data and minimize file storage cost. Which two actions should you take?
A. Create a Cloud Storage lifecycle rule with Age: ג€30ג€, Storage Class: ג€Standardג€, and Action: ג€Set to Coldlineג€, and create a second GCS life-cycle rule with Age: ג€365ג€, Storage Class: ג€Coldlineג€, and Action: ג€Deleteג€.
B. Create a Cloud Storage lifecycle rule with Age: ג€30ג€, Storage Class: ג€Coldlineג€, and Action: ג€Set to Nearlineג€, and create a second GCS life-cycle rule with Age: ג€91ג€, Storage Class: ג€Coldlineג€, and Action: ג€Set to Nearlineג€.
C. Create a Cloud Storage lifecycle rule with Age: ג€90ג€, Storage Class: ג€Standardג€, and Action: ג€Set to Nearlineג€, and create a second GCS life-cycle rule with Age: ג€91ג€, Storage Class: ג€Nearlineג€, and Action: ג€Set to Coldlineג€.
D. Create a Cloud Storage lifecycle rule with Age: ג€30ג€, Storage Class: ג€Standardג€, and Action: ג€Set to Coldlineג€, and create a second GCS life-cycle rule with Age: ג€365ג€, Storage Class: ג€Nearlineג€, and Action: ג€Deleteג€.
Your company wants you to build a highly reliable web application with a few public APIs as the backend. You don't expect a lot of user traffic, but traffic could spike occasionally. You want to leverage Cloud Load Balancing, and the solution must be cost-effective for users. What should you do?
A. Store static content such as HTML and images in Cloud CDN. Host the APIs on App Engine and store the user data in Cloud SQL.
B. Store static content such as HTML and images in a Cloud Storage bucket. Host the APIs on a zonal Google Kubernetes Engine cluster with worker nodes in multiple zones, and save the user data in Cloud Spanner.
C. Store static content such as HTML and images in Cloud CDN. Use Cloud Run to host the APIs and save the user data in Cloud SQL.
D. Store static content such as HTML and images in a Cloud Storage bucket. Use Cloud Functions to host the APIs and save the user data in Firestore.
You are creating an App Engine application that uses Cloud Datastore as its persistence layer. You need to retrieve several root entities for which you have the identifiers. You want to minimize the overhead in operations performed by Cloud Datastore. What should you do?
A. Create the Key object for each Entity and run a batch get operation
B. Create the Key object for each Entity and run multiple get operations, one operation for each entity
C. Use the identifiers to create a query filter and run a batch query operation
D. Use the identifiers to create a query filter and run multiple query operations, one operation for each entity
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.
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. As part of Dress4Win's plans to migrate to the cloud, they want to be able to set up a managed logging and monitoring system so they can handle spikes in their traffic load. They want to ensure that: * The infrastructure can be notified when it needs to scale up and down to handle the ebb and flow of usage throughout the day * Their administrators are notified automatically when their application reports errors. * They can filter their aggregated logs down in order to debug one piece of the application across many hosts Which Google StackDriver features should they use?
A. Logging, Alerts, Insights, Debug
B. Monitoring, Trace, Debug, Logging
C. Monitoring, Logging, Alerts, Error Reporting
D. Monitoring, Logging, Debug, Error Report
A small number of API requests to your microservices-based application take a very long time. You know that each request to the API can traverse many services. You want to know which service takes the longest in those cases. What should you do?
A. Set timeouts on your application so that you can fail requests faster
B. Send custom metrics for each of your requests to Stackdriver Monitoring
C. Use Stackdriver Monitoring to look for insights that show when your API latencies are high
D. Instrument your application with Stackdriver Trace in order to break down the request latencies at each microservice
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.
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
One of your primary business objectives is being able to trust the data stored in your application. You want to log all changes to the application data. How can you design your logging system to verify authenticity of your logs?
A. Write the log concurrently in the cloud and on premises
B. Use a SQL database and limit who can modify the log table
C. Digitally sign each timestamp and log entry and store the signature
D. Create a JSON dump of each log entry and store it in Google Cloud Storage
A development manager is building a new application. He asks you to review his requirements and identify what cloud technologies he can use to meet them. The application must: 1. Be based on open-source technology for cloud portability 2. Dynamically scale compute capacity based on demand 3. Support continuous software delivery 4. Run multiple segregated copies of the same application stack 5. Deploy application bundles using dynamic templates 6. Route network traffic to specific services based on URL Which combination of technologies will meet all of his requirements?
A. Google Kubernetes Engine, Jenkins, and Helm
B. Google Kubernetes Engine and Cloud Load Balancing
C. Google Kubernetes Engine and Cloud Deployment Manager
D. Google Kubernetes Engine, Jenkins, and Cloud Load Balancing
Your company acquired a healthcare startup and must retain its customers' medical information for up to 4 more years, depending on when it was created. Your corporate policy is to securely retain this data, and then delete it as soon as regulations allow. Which approach should you take?
A. Store the data in Google Drive and manually delete records as they expire.
B. Anonymize the data using the Cloud Data Loss Prevention API and store it indefinitely.
C. Store the data in Cloud Storage and use lifecycle management to delete files when they expire.
D. Store the data in Cloud Storage and run a nightly batch script that deletes all expired data.
Your company has developed a monolithic, 3-tier application to allow external users to upload and share files. The solution cannot be easily enhanced and lacks reliability. The development team would like to re-architect the application to adopt microservices and a fully managed service approach, but they need to convince their leadership that the effort is worthwhile. Which advantage(s) should they highlight to leadership?
A. The new approach will be significantly less costly, make it easier to manage the underlying infrastructure, and automatically manage the CI/CD pipelines.
B. The monolithic solution can be converted to a container with Docker. The generated container can then be deployed into a Kubernetes cluster.
C. The new approach will make it easier to decouple infrastructure from application, develop and release new features, manage the underlying infrastructure, manage CI/CD pipelines and perform A/B testing, and scale the solution if necessary.
D. The process can be automated with Migrate for Compute Engine.
Your company is building a new architecture to support its data-centric business focus. You are responsible for setting up the network. Your company's mobile and web-facing applications will be deployed on-premises, and all data analysis will be conducted in GCP. The plan is to process and load 7 years of archived .csv files totaling 900 TB of data and then continue loading 10 TB of data daily. You currently have an existing 100-MB internet connection. What actions will meet your company's needs?
A. Compress and upload both archived files and files uploaded daily using the gsutil ג€”m option.
B. Lease a Transfer Appliance, upload archived files to it, and send it to Google to transfer archived data to Cloud Storage. Establish a connection with Google using a Dedicated Interconnect or Direct Peering connection and use it to upload files daily.
C. Lease a Transfer Appliance, upload archived files to it, and send it to Google to transfer archived data to Cloud Storage. Establish one Cloud VPN Tunnel to VPC networks over the public internet, and compress and upload files daily using the gsutil ג€”m option.
D. Lease a Transfer Appliance, upload archived files to it, and send it to Google to transfer archived data to Cloud Storage. Establish a Cloud VPN Tunnel to VPC networks over the public internet, and compress and upload files daily.
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. A new architecture that writes all incoming data to BigQuery has been introduced. You notice that the data is dirty, and want to ensure data quality on an automated daily basis while managing cost. What should you do?
A. Set up a streaming Cloud Dataflow job, receiving data by the ingestion process. Clean the data in a Cloud Dataflow pipeline.
B. Create a Cloud Function that reads data from BigQuery and cleans it. Trigger the Cloud Function from a Compute Engine instance.
C. Create a SQL statement on the data in BigQuery, and save it as a view. Run the view daily, and save the result to a new table.
D. Use Cloud Dataprep and configure the BigQuery tables as the source. Schedule a daily job to clean the data.
You want to establish a Compute Engine application in a single VPC across two regions. The application must communicate over VPN to an on-premises network. How should you deploy the VPN?
A. Use VPC Network Peering between the VPC and the on-premises network.
B. Expose the VPC to the on-premises network using IAM and VPC Sharing.
C. Create a global Cloud VPN Gateway with VPN tunnels from each region to the on-premises peer gateway.
D. Deploy Cloud VPN Gateway in each region. Ensure that each region has at least one VPN tunnel to the on-premises peer gateway.
You have deployed an application on Anthos clusters (formerly Anthos GKE). According to the SRE practices at your company, you need to be alerted if request latency is above a certain threshold for a specified amount of time. What should you do?
A. Install Anthos Service Mesh on your cluster. Use the Google Cloud Console to define a Service Level Objective (SLO), and create an alerting policy based on this SLO.
B. Enable the Cloud Trace API on your project, and use Cloud Monitoring Alerts to send an alert based on the Cloud Trace metrics.
C. Use Cloud Profiler to follow up the request latency. Create a custom metric in Cloud Monitoring based on the results of Cloud Profiler, and create an Alerting policy in case this metric exceeds the threshold.
D. Configure Anthos Config Management on your cluster, and create a yaml file that defines the SLO and alerting policy you want to deploy in your cluster.
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 plans to migrate a multi-petabyte data set to the cloud. The data set must be available 24hrs a day. Your business analysts have experience only with using a SQL interface. How should you store the data to optimize it for ease of analysis?
A. Load data into Google BigQuery
B. Insert data into Google Cloud SQL
C. Put flat files into Google Cloud Storage
D. Stream data into Google Cloud Datastore
An application development team believes their current logging tool will not meet their needs for their new cloud-based product. They want a better tool to capture errors and help them analyze their historical log data. You want to help them find a solution that meets their needs. What should you do?
A. Direct them to download and install the Google StackDriver logging agent
B. Send them a list of online resources about logging best practices
C. Help them define their requirements and assess viable logging tools
D. Help them upgrade their current tool to take advantage of any new features
You have created several pre-emptible Linux virtual machine instances using Google Compute Engine. You want to properly shut down your application before the virtual machines are preempted. What should you do?
A. Create a shutdown script named k99.shutdown in the /etc/rc.6.d/ directory
B. Create a shutdown script registered as a xinetd service in Linux and configure a Stackdriver endpoint check to call the service
C. Create a shutdown script and use it as the value for a new metadata entry with the key shutdown-script in the Cloud Platform Console when you create the new virtual machine instance
D. Create a shutdown script, registered as a xinetd service in Linux, and use the gcloud compute instances add-metadata command to specify the service URL as the value for a new metadata entry with the key shutdown-script-url
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
You are using Cloud CDN to deliver static HTTP(S) website content hosted on a Compute Engine instance group. You want to improve the cache hit ratio. What should you do?
A. Customize the cache keys to omit the protocol from the key.
B. Shorten the expiration time of the cached objects.
C. Make sure the HTTP(S) header ג€Cache-Regionג€ points to the closest region of your users.
D. Replicate the static content in a Cloud Storage bucket. Point CloudCDN toward a load balancer on that bucket.
Your applications will be writing their logs to BigQuery for analysis. Each application should have its own table. Any logs older than 45 days should be removed. You want to optimize storage and follow Google-recommended practices. What should you do?
A. Configure the expiration time for your tables at 45 days
B. Make the tables time-partitioned, and configure the partition expiration at 45 days
C. Rely on BigQuery’s default behavior to prune application logs older than 45 days
D. Create a script that uses the BigQuery command line tool (bq) to remove records older than 45 days
A development team at your company has created a dockerized HTTPS web application. You need to deploy the application on Google Kubernetes Engine (GKE) and make sure that the application scales automatically. How should you deploy to GKE?
A. Use the Horizontal Pod Autoscaler and enable cluster autoscaling. Use an Ingress resource to load-balance the HTTPS traffic.
B. Use the Horizontal Pod Autoscaler and enable cluster autoscaling on the Kubernetes cluster. Use a Service resource of type LoadBalancer to load-balance the HTTPS traffic.
C. Enable autoscaling on the Compute Engine instance group. Use an Ingress resource to load-balance the HTTPS traffic.
D. Enable autoscaling on the Compute Engine instance group. Use a Service resource of type LoadBalancer to load-balance the HTTPS traffic.
Your web application has several VM instances running within a VPC. You want to restrict communications between instances to only the paths and ports you authorize, but you don't want to rely on static IP addresses or subnets because the app can autoscale. How should you restrict communications?
A. Use separate VPCs to restrict traffic
B. Use firewall rules based on network tags attached to the compute instances
C. Use Cloud DNS and only allow connections from authorized hostnames
D. Use service accounts and configure the web application to authorize particular service accounts to have access
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 company has a networking team and a development team. The development team runs applications on Compute Engine instances that contain sensitive data. The development team requires administrative permissions for Compute Engine. Your company requires all network resources to be managed by the networking team. The development team does not want the networking team to have access to the sensitive data on the instances. What should you do?
A. 1. Create a project with a standalone VPC and assign the Network Admin role to the networking team. 2. Create a second project with a standalone VPC and assign the Compute Admin role to the development team. 3. Use Cloud VPN to join the two VPCs.
B. 1. Create a project with a standalone Virtual Private Cloud (VPC), assign the Network Admin role to the networking team, and assign the Compute Admin role to the development team.
C. 1. Create a project with a Shared VPC and assign the Network Admin role to the networking team. 2. Create a second project without a VPC, configure it as a Shared VPC service project, and assign the Compute Admin role to the development team.
D. 1. Create a project with a standalone VPC and assign the Network Admin role to the networking team. 2. Create a second project with a standalone VPC and assign the Compute Admin role to the development team. 3. Use VPC Peering to join the two VPCs.
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.
All Compute Engine instances in your VPC should be able to connect to an Active Directory server on specific ports. Any other traffic emerging from your instances is not allowed. You want to enforce this using VPC firewall rules. How should you configure the firewall rules?
A. Create an egress rule with priority 1000 to deny all traffic for all instances. Create another egress rule with priority 100 to allow the Active Directory traffic for all instances.
B. Create an egress rule with priority 100 to deny all traffic for all instances. Create another egress rule with priority 1000 to allow the Active Directory traffic for all instances.
C. Create an egress rule with priority 1000 to allow the Active Directory traffic. Rely on the implied deny egress rule with priority 100 to block all traffic for all instances.
D. Create an egress rule with priority 100 to allow the Active Directory traffic. Rely on the implied deny egress rule with priority 1000 to block all traffic for all instances.
Your team will start developing a new application using microservices architecture on Kubernetes Engine. As part of the development lifecycle, any code change that has been pushed to the remote develop branch on your GitHub repository should be built and tested automatically. When the build and test are successful, the relevant microservice will be deployed automatically in the development environment. You want to ensure that all code deployed in the development environment follows this process. What should you do?
A. Have each developer install a pre-commit hook on their workstation that tests the code and builds the container when committing on the development branch. After a successful commit, have the developer deploy the newly built container image on the development cluster.
B. Install a post-commit hook on the remote git repository that tests the code and builds the container when code is pushed to the development branch. After a successful commit, have the developer deploy the newly built container image on the development cluster.
C. Create a Cloud Build trigger based on the development branch that tests the code, builds the container, and stores it in Container Registry. Create a deployment pipeline that watches for new images and deploys the new image on the development cluster. Ensure only the deployment tool has access to deploy new versions.
D. Create a Cloud Build trigger based on the development branch to build a new container image and store it in Container Registry. Rely on Vulnerability Scanning to ensure the code tests succeed. As the final step of the Cloud Build process, deploy the new container image on the development cluster. Ensure only Cloud Build has access to deploy new versions.
You have an App Engine application that needs to be updated. You want to test the update with production traffic before replacing the current application version. What should you do?
A. Deploy the update using the Instance Group Updater to create a partial rollout, which allows for canary testing.
B. Deploy the update as a new version in the App Engine application, and split traffic between the new and current versions.
C. Deploy the update in a new VPC, and use Google’s global HTTP load balancing to split traffic between the update and current applications.
D. Deploy the update as a new App Engine application, and use Google’s global HTTP load balancing to split traffic between the new and current applications.
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.
The migration of JencoMart’s application to Google Cloud Platform (GCP) is progressing too slowly. The infrastructure is shown in the diagram. You want to maximize throughput.
What are three potential bottlenecks? (Choose three.)
A. A single VPN tunnel, which limits throughput
B. A tier of Google Cloud Storage that is not suited for this task
C. A copy command that is not suited to operate over long distances
D. Fewer virtual machines (VMs) in GCP than on-premises machines
E. A separate storage layer outside the VMs, which is not suited for this task
F. Complicated internet connectivity between the on-premises infrastructure and GCP
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. Your development teams release new versions of games running on Google Kubernetes Engine (GKE) daily. You want to create service level indicators (SLIs) to evaluate the quality of the new versions from the user's perspective. What should you do?
A. Create CPU Utilization and Request Latency as service level indicators.
B. Create GKE CPU Utilization and Memory Utilization as service level indicators.
C. Create Request Latency and Error Rate as service level indicators.
D. Create Server Uptime and Error Rate as service level indicators.
Your company uses Google Kubernetes Engine (GKE) as a platform for all workloads. Your company has a single large GKE cluster that contains batch, stateful, and stateless workloads. The GKE cluster is configured with a single node pool with 200 nodes. Your company needs to reduce the cost of this cluster but does not want to compromise availability. What should you do?
A. Create a second GKE cluster for the batch workloads only. Allocate the 200 original nodes across both clusters.
B. Configure CPU and memory limits on the namespaces in the cluster. Configure all Pods to have a CPU and memory limits.
C. Configure a HorizontalPodAutoscaler for all stateless workloads and for all compatible stateful workloads. Configure the cluster to use node auto scaling.
D. Change the node pool to use preemptible VMs.
Your company has announced that they will be outsourcing operations functions. You want to allow developers to easily stage new versions of a cloud-based application in the production environment and allow the outsourced operations team to autonomously promote staged versions to production. You want to minimize the operational overhead of the solution. Which Google Cloud product should you migrate to?
A. App Engine
B. GKE On-Prem
C. Compute Engine
D. Google Kubernetes Engine
You have been asked to select the storage system for the click-data of your company's large portfolio of websites. This data is streamed in from a custom website analytics package at a typical rate of 6,000 clicks per minute. With bursts of up to 8,500 clicks per second. It must have been stored for future analysis by your data science and user experience teams. Which storage infrastructure should you choose?
A. Google Cloud SQL
B. Google Cloud Bigtable
C. Google Cloud Storage
D. Google Cloud Datastore
You want to automate the creation of a managed instance group. The VMs have many OS package dependencies. You want to minimize the startup time for new VMs in the instance group. What should you do?
A. Use Terraform to create the managed instance group and a startup script to install the OS package dependencies.
B. Create a custom VM image with all OS package dependencies. Use Deployment Manager to create the managed instance group with the VM image.
C. Use Puppet to create the managed instance group and install the OS package dependencies.
D. Use Deployment Manager to create the managed instance group and Ansible to install the OS package dependencies.
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. Mountkirk Games wants to migrate from their current analytics and statistics reporting model to one that meets their technical requirements on Google Cloud Platform. Which two steps should be part of their migration plan? (Choose two.)
A. Evaluate the impact of migrating their current batch ETL code to Cloud Dataflow.
B. Write a schema migration plan to denormalize data for better performance in BigQuery.
C. Draw an architecture diagram that shows how to move from a single MySQL database to a MySQL cluster.
D. Load 10 TB of analytics data from a previous game into a Cloud SQL instance, and run test queries against the full dataset to confirm that they complete successfully.
E. Integrate Cloud Armor to defend against possible SQL injection attacks in analytics files uploaded to Cloud Storage.
You have developed a non-critical update to your application that is running in a managed instance group, and have created a new instance template with the update that you want to release. To prevent any possible impact to the application, you don't want to update any running instances. You want any new instances that are created by the managed instance group to contain the new update. What should you do?
A. Start a new rolling restart operation.
B. Start a new rolling replace operation.
C. Start a new rolling update. Select the Proactive update mode.
D. Start a new rolling update. Select the Opportunistic update mode.
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