A company uses Amazon Athena for one-time queries against data that is in Amazon S3. The company has several use cases. The company must implement permission controls to separate query processes and access to query history among users, teams, and applications that are in the same AWS account.
Which solution will meet these requirements?

A. Create an S3 bucket for each use case. Create an S3 bucket policy that grants permissions to appropriate individual IAM users. Apply the S3 bucket policy to the S3 bucket.

B. Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an IAM policy that uses the tags to apply appropriate permissions to the workgroup.

C. Create an IAM role for each use case. Assign appropriate permissions to the role for each use case. Associate the role with Athena.

D. Create an AWS Glue Data Catalog resource policy that grants permissions to appropriate individual IAM users for each use case. Apply the resource policy to the specific tables that Athena uses.

A company maintains an Amazon Redshift provisioned cluster that the company uses for extract, transform, and load (ETL) operations to support critical analysis tasks. A sales team within the company maintains a Redshift cluster that the sales team uses for business intelligence (BI) tasks.
The sales team recently requested access to the data that is in the ETL Redshift cluster so the team can perform weekly summary analysis tasks. The sales team needs to join data from the ETL cluster with data that is in the sales team's BI cluster.
The company needs a solution that will share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution must minimize usage of the computing resources of the ETL cluster.
Which solution will meet these requirements?

A. Set up the sales team BI cluster as a consumer of the ETL cluster by using Redshift data sharing.

B. Create materialized views based on the sales team’s requirements. Grant the sales team direct access to the ETL cluster.

C. Create database views based on the sales team’s requirements. Grant the sales team direct access to the ETL cluster.

D. Unload a copy of the data from the ETL cluster to an Amazon S3 bucket every week. Create an Amazon Redshift Spectrum table based on the content of the ETL cluster.

A company has a data warehouse that contains a table that is named Sales. The company stores the table in Amazon Redshift. The table includes a column that is named city_name. The company wants to query the table to find all rows that have a city_name that starts with "San" or "El".
Which SQL query will meet this requirement?

A. Select * from Sales where city_name ~ ‘$(San|El)*’;

B. Select * from Sales where city_name ~ ‘^(San|El)*’;

C. Select * from Sales where city_name ~’$(San&El)*’;

D. Select * from Sales where city_name ~ ‘^(San&El)*’;

A data engineer runs Amazon Athena queries on data that is in an Amazon S3 bucket. The Athena queries use AWS Glue Data Catalog as a metadata table.
The data engineer notices that the Athena query plans are experiencing a performance bottleneck. The data engineer determines that the cause of the performance bottleneck is the large number of partitions that are in the S3 bucket. The data engineer must resolve the performance bottleneck and reduce Athena query planning time.
Which solutions will meet these requirements? (Choose two.)

A. Create an AWS Glue partition index. Enable partition filtering.

B. Bucket the data based on a column that the data have in common in a WHERE clause of the user query.

C. Use Athena partition projection based on the S3 bucket prefix.

D. Transform the data that is in the S3 bucket to Apache Parquet format.

E. Use the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects.

An online retail company stores Application Load Balancer (ALB) access logs in an Amazon S3 bucket. The company wants to use Amazon Athena to query the logs to analyze traffic patterns.
A data engineer creates an unpartitioned table in Athena. As the amount of the data gradually increases, the response time for queries also increases. The data engineer wants to improve the query performance in Athena.
Which solution will meet these requirements with the LEAST operational effort?

A. Create an AWS Glue job that determines the schema of all ALB access logs and writes the partition metadata to AWS Glue Data Catalog.

B. Create an AWS Glue crawler that includes a classifier that determines the schema of all ALB access logs and writes the partition metadata to AWS Glue Data Catalog.

C. Create an AWS Lambda function to transform all ALB access logs. Save the results to Amazon S3 in Apache Parquet format. Partition the metadata. Use Athena to query the transformed data.

D. Use Apache Hive to create bucketed tables. Use an AWS Lambda function to transform all ALB access logs.

A company uses Amazon RDS for MySQL as the database for a critical application. The database workload is mostly writes, with a small number of reads.
A data engineer notices that the CPU utilization of the DB instance is very high. The high CPU utilization is slowing down the application. The data engineer must reduce the CPU utilization of the DB Instance.
Which actions should the data engineer take to meet this requirement? (Choose two.)

A. Use the Performance Insights feature of Amazon RDS to identify queries that have high CPU utilization. Optimize the problematic queries.

B. Modify the database schema to include additional tables and indexes.

C. Reboot the RDS DB instance once each week.

D. Upgrade to a larger instance size.

E. Implement caching to reduce the database query load.

An application consumes messages from an Amazon Simple Queue Service (Amazon SQS) queue. The application experiences occasional downtime. As a result of the downtime, messages within the queue expire and are deleted after 1 day. The message deletions cause data loss for the application.
Which solutions will minimize data loss for the application? (Choose two.)

A. Increase the message retention period

B. Increase the visibility timeout.

C. Attach a dead-letter queue (DLQ) to the SQS queue.

D. Use a delay queue to delay message delivery

E. Reduce message processing time.

A company is migrating its database servers from Amazon EC2 instances that run Microsoft SQL Server to Amazon RDS for Microsoft SQL Server DB instances. The company's analytics team must export large data elements every day until the migration is complete. The data elements are the result of SQL joins across multiple tables. The data must be in Apache Parquet format. The analytics team must store the data in Amazon S3.
Which solution will meet these requirements in the MOST operationally efficient way?

A. Create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create an AWS Glue job that selects the data directly from the view and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.

B. Schedule SQL Server Agent to run a daily SQL query that selects the desired data elements from the EC2 instance-based SQL Server databases. Configure the query to direct the output .csv objects to an S3 bucket. Create an S3 event that invokes an AWS Lambda function to transform the output format from .csv to Parquet.

C. Use a SQL query to create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create and run an AWS Glue crawler to read the view. Create an AWS Glue job that retrieves the data and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.

D. Create an AWS Lambda function that queries the EC2 instance-based databases by using Java Database Connectivity (JDBC). Configure the Lambda function to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. Use Amazon EventBridge to schedule the Lambda function to run every day.

A company reads data from customer databases that run on Amazon RDS. The databases contain many inconsistent fields. For example, a customer record field that iPnamed place_id in one database is named location_id in another database. The company needs to link customer records across different databases, even when customer record fields do not match.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create a provisioned Amazon EMR cluster to process and analyze data in the databases. Connect to the Apache Zeppelin notebook. Use the FindMatches transform to find duplicate records in the data.

B. Create an AWS Glue crawler to craw the databases. Use the FindMatches transform to find duplicate records in the data. Evaluate and tune the transform by evaluating the performance and results.

C. Create an AWS Glue crawler to craw the databases. Use Amazon SageMaker to construct Apache Spark ML pipelines to find duplicate records in the data.

D. Create a provisioned Amazon EMR cluster to process and analyze data in the databases. Connect to the Apache Zeppelin notebook. Use an Apache Spark ML model to find duplicate records in the data. Evaluate and tune the model by evaluating the performance and results.

A manufacturing company has many IoT devices in facilities around the world. The company uses Amazon Kinesis Data Streams to collect data from the devices. The data includes device ID, capture date, measurement type, measurement value, and facility ID. The company uses facility ID as the partition key.
The company's operations team recently observed many WriteThroughputExceeded exceptions. The operations team found that some shards were heavily used but other shards were generally idle.
How should the company resolve the issues that the operations team observed?

A. Change the partition key from facility ID to a randomly generated key.

B. Increase the number of shards.

C. Archive the data on the producer’s side.

D. Change the partition key from facility ID to capture date.

A company receives call logs as Amazon S3 objects that contain sensitive customer information. The company must protect the S3 objects by using encryption. The company must also use encryption keys that only specific employees can access.
Which solution will meet these requirements with the LEAST effort?

A. Use an AWS CloudHSM cluster to store the encryption keys. Configure the process that writes to Amazon S3 to make calls to CloudHSM to encrypt and decrypt the objects. Deploy an IAM policy that restricts access to the CloudHSM cluster.

B. Use server-side encryption with customer-provided keys (SSE-C) to encrypt the objects that contain customer information. Restrict access to the keys that encrypt the objects.

C. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the KMS keys that encrypt the objects.

D. Use server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the Amazon S3 managed keys that encrypt the objects.

A company receives test results from testing facilities that are located around the world. The company stores the test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer needs to process the files, convert them into Apache Parquet format, and load them into Amazon Redshift tables. The data engineer uses AWS Glue to process the files, AWS Step Functions to orchestrate the processes, and Amazon EventBridge to schedule jobs.
The company recently added more testing facilities. The time required to process files is increasing. The data engineer must reduce the data processing time.
Which solution will MOST reduce the data processing time?

A. Use AWS Lambda to group the raw input files into larger files. Write the larger files back to Amazon S3. Use AWS Glue to process the files. Load the files into the Amazon Redshift tables.

B. Use the AWS Glue dynamic frame file-grouping option to ingest the raw input files. Process the files. Load the files into the Amazon Redshift tables.

C. Use the Amazon Redshift COPY command to move the raw input files from Amazon S3 directly into the Amazon Redshift tables. Process the files in Amazon Redshift.

D. Use Amazon EMR instead of AWS Glue to group the raw input files. Process the files in Amazon EMR. Load the files into the Amazon Redshift tables.

A company stores 10 to 15 TB of uncompressed .csv files in Amazon S3. The company is evaluating Amazon Athena as a one-time query engine.
The company wants to transform the data to optimize query runtime and storage costs.
Which file format and compression solution will meet these requirements for Athena queries?

A. .csv format compressed with zip

B. JSON format compressed with bzip2

C. Apache Parquet format compressed with Snappy

D. Apache Avro format compressed with LZO

A company has a business intelligence platform on AWS. The company uses an AWS Storage Gateway Amazon S3 File Gateway to transfer files from the company's on-premises environment to an Amazon S3 bucket.
A data engineer needs to setup a process that will automatically launch an AWS Glue workflow to run a series of AWS Glue jobs when each file transfer finishes successfully.
Which solution will meet these requirements with the LEAST operational overhead?

A. Determine when the file transfers usually finish based on previous successful file transfers. Set up an Amazon EventBridge scheduled event to initiate the AWS Glue jobs at that time of day.

B. Set up an Amazon EventBridge event that initiates the AWS Glue workflow after every successful S3 File Gateway file transfer event.

C. Set up an on-demand AWS Glue workflow so that the data engineer can start the AWS Glue workflow when each file transfer is complete.

D. Set up an AWS Lambda function that will invoke the AWS Glue Workflow. Set up an event for the creation of an S3 object as a trigger for the Lambda function.

An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures.
The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date.
As the amount of data increases, the company wants to optimize the storage solution to improve query performance.
Which combination of solutions will meet these requirements? (Choose two.)

A. Add a randomized string to the beginning of the keys in Amazon S3 to get more throughput across partitions.

B. Use an S3 bucket that is in the same account that uses Athena to query the data.

C. Use an S3 bucket that is in the same AWS Region where the company runs Athena queries.

D. Preprocess the .csv data to JSON format by fetching only the document keys that the query requires.

E. Preprocess the .csv data to Apache Parquet format by fetching only the data blocks that are needed for predicates.

A financial company wants to implement a data mesh. The data mesh must support centralized data governance, data analysis, and data access control. The company has decided to use AWS Glue for data catalogs and extract, transform, and load (ETL) operations.
Which combination of AWS services will implement a data mesh? (Choose two.)

A. Use Amazon Aurora for data storage. Use an Amazon Redshift provisioned cluster for data analysis.

B. Use Amazon S3 for data storage. Use Amazon Athena for data analysis.

C. Use AWS Glue DataBrew for centralized data governance and access control.

D. Use Amazon RDS for data storage. Use Amazon EMR for data analysis.

E. Use AWS Lake Formation for centralized data governance and access control.

A media company wants to use Amazon OpenSearch Service to analyze rea-time data about popular musical artists and songs. The company expects to ingest millions of new data events every day. The new data events will arrive through an Amazon Kinesis data stream. The company must transform the data and then ingest the data into the OpenSearch Service domain.
Which method should the company use to ingest the data with the LEAST operational overhead?

A. Use Amazon Kinesis Data Firehose and an AWS Lambda function to transform the data and deliver the transformed data to OpenSearch Service.

B. Use a Logstash pipeline that has prebuilt filters to transform the data and deliver the transformed data to OpenSearch Service.

C. Use an AWS Lambda function to call the Amazon Kinesis Agent to transform the data and deliver the transformed data OpenSearch Service.

D. Use the Kinesis Client Library (KCL) to transform the data and deliver the transformed data to OpenSearch Service.

A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket.
The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility.
Which solution will meet these requirements with the LOWEST latency?

A. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.

B. Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard.

C. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Create a new Data Firehose delivery stream to publish data directly to an Amazon Timestream database. Use the Timestream database as a source to create an Amazon QuickSight dashboard.

D. Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.

A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the query does not return results for all of the products that are in the sales_data table. The data engineer needs to troubleshoot the query to resolve the issue.
The data engineer's original query is as follows:
SELECT product_name, sum(sales_amount)
FROM sales_data -
WHERE year = 2023 -
GROUP BY product_name -
How should the data engineer modify the Athena query to meet these requirements?

A. Replace sum(sales_amount) with count(*) for the aggregation.

B. Change WHERE year = 2023 to WHERE extract(year FROM sales_data) = 2023.

C. Add HAVING sum(sales_amount) > 0 after the GROUP BY clause.

D. Remove the GROUP BY clause.

A company's data engineer needs to optimize the performance of table SQL queries. The company stores data in an Amazon Redshift cluster. The data engineer cannot increase the size of the cluster because of budget constraints.
The company stores the data in multiple tables and loads the data by using the EVEN distribution style. Some tables are hundreds of gigabytes in size. Other tables are less than 10 MB in size.
Which solution will meet these requirements?

A. Keep using the EVEN distribution style for all tables. Specify primary and foreign keys for all tables.

B. Use the ALL distribution style for large tables. Specify primary and foreign keys for all tables.

C. Use the ALL distribution style for rarely updated small tables. Specify primary and foreign keys for all tables.

D. Specify a combination of distribution, sort, and partition keys for all tables.

A data engineer is configuring Amazon SageMaker Studio to use AWS Glue interactive sessions to prepare data for machine learning (ML) models.
The data engineer receives an access denied error when the data engineer tries to prepare the data by using SageMaker Studio.
Which change should the engineer make to gain access to SageMaker Studio?

A. Add the AWSGlueServiceRole managed policy to the data engineer’s IAM user.

B. Add a policy to the data engineer’s IAM user that includes the sts:AssumeRole action for the AWS Glue and SageMaker service principals in the trust policy.

C. Add the AmazonSageMakerFullAccess managed policy to the data engineer’s IAM user.

D. Add a policy to the data engineer’s IAM user that allows the sts:AddAssociation action for the AWS Glue and SageMaker service principals in the trust policy.

A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform.
The company wants to minimize the effort and time required to incorporate third-party datasets.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use API calls to access and integrate third-party datasets from AWS Data Exchange.

B. Use API calls to access and integrate third-party datasets from AWS DataSync.

C. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories.

D. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR).

A company uses AWS Step Functions to orchestrate a data pipeline. The pipeline consists of Amazon EMR jobs that ingest data from data sources and store the data in an Amazon S3 bucket. The pipeline also includes EMR jobs that load the data to Amazon Redshift.
The company's cloud infrastructure team manually built a Step Functions state machine. The cloud infrastructure team launched an EMR cluster into a VPC to support the EMR jobs. However, the deployed Step Functions state machine is not able to run the EMR jobs.
Which combination of steps should the company take to identify the reason the Step Functions state machine is not able to run the EMR jobs? (Choose two.)

A. Use AWS CloudFormation to automate the Step Functions state machine deployment. Create a step to pause the state machine during the EMR jobs that fail. Configure the step to wait for a human user to send approval through an email message. Include details of the EMR task in the email message for further analysis.

B. Verify that the Step Functions state machine code has all IAM permissions that are necessary to create and run the EMR jobs. Verify that the Step Functions state machine code also includes IAM permissions to access the Amazon S3 buckets that the EMR jobs use. Use Access Analyzer for S3 to check the S3 access properties.

C. Check for entries in Amazon CloudWatch for the newly created EMR cluster. Change the AWS Step Functions state machine code to use Amazon EMR on EKS. Change the IAM access policies and the security group configuration for the Step Functions state machine code to reflect inclusion of Amazon Elastic Kubernetes Service (Amazon EKS).

D. Query the flow logs for the VPC. Determine whether the traffic that originates from the EMR cluster can successfully reach the data providers. Determine whether any security group that might be attached to the Amazon EMR cluster allows connections to the data source servers on the informed ports.

E. Check the retry scenarios that the company configured for the EMR jobs. Increase the number of seconds in the interval between each EMR task. Validate that each fallback state has the appropriate catch for each decision state. Configure an Amazon Simple Notification Service (Amazon SNS) topic to store the error messages.

A company stores employee data in Amazon Resdshift. A table names Employee uses columns named Region ID, Department ID, and Role ID as a compound sort key.
Which queries will MOST increase the speed of query by using a compound sort key of the table? (Choose two.)

A. Select *from Employee where Region ID=’North America’;

B. Select *from Employee where Region ID=’North America’ and Department ID=20;

C. Select *from Employee where Department ID=20 and Region ID=’North America’;

D. Select *from Employee where Role ID=50;

E. Select *from Employee where Region ID=’North America’ and Role ID=50;

A media company uses software as a service (SaaS) applications to gather data by using third-party tools. The company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform analytics based on the data.
Which AWS service or feature will meet these requirements with the LEAST operational overhead?

A. Amazon Managed Streaming for Apache Kafka (Amazon MSK)

B. Amazon AppFlow

C. AWS Glue Data Catalog

D. Amazon Kinesis

A marketing company uses Amazon S3 to store clickstream data. The company queries the data at the end of each day by using a SQL JOIN clause on S3 objects that are stored in separate buckets.
The company creates key performance indicators (KPIs) based on the objects. The company needs a serverless solution that will give users the ability to query data by partitioning the data. The solution must maintain the atomicity, consistency, isolation, and durability (ACID) properties of the data.
Which solution will meet these requirements MOST cost-effectively?

A. Amazon S3 Select

B. Amazon Redshift Spectrum

C. Amazon Athena

D. Amazon EMR

A company is using an AWS Transfer Family server to migrate data from an on-premises environment to AWS. Company policy mandates the use of TLS 1.2 or above to encrypt the data in transit.
Which solution will meet these requirements?

A. Generate new SSH keys for the Transfer Family server. Make the old keys and the new keys available for use.

B. Update the security group rules for the on-premises network to allow only connections that use TLS 1.2 or above.

C. Update the security policy of the Transfer Family server to specify a minimum protocol version of TLS 1.2

D. Install an SSL certificate on the Transfer Family server to encrypt data transfers by using TLS 1.2.

A company wants to migrate data from an Amazon RDS for PostgreSQL DB instance in the eu-east-1 Region of an AWS account named Account_A. The company will migrate the data to an Amazon Redshift cluster in the eu-west-1 Region of an AWS account named Account_B.
Which solution will give AWS Database Migration Service (AWS DMS) the ability to replicate data between two data stores?

A. Set up an AWS DMS replication instance in Account_B in eu-west-1.

B. Set up an AWS DMS replication instance in Account_B in eu-east-1.

C. Set up an AWS DMS replication instance in a new AWS account in eu-west-1.

D. Set up an AWS DMS replication instance in Account_A in eu-east-1.

A retail company stores transactions, store locations, and customer information tables in four reserved ra3.4xlarge Amazon Redshift cluster nodes. All three tables use even table distribution.
The company updates the store location table only once or twice every few years.
A data engineer notices that Redshift queues are slowing down because the whole store location table is constantly being broadcast to all four compute nodes for most queries. The data engineer wants to speed up the query performance by minimizing the broadcasting of the store location table.
Which solution will meet these requirements in the MOST cost-effective way?

A. Change the distribution style of the store location table from EVEN distribution to ALL distribution.

B. Change the distribution style of the store location table to KEY distribution based on the column that has the highest dimension.

C. Add a join column named store_id into the sort key for all the tables.

D. Upgrade the Redshift reserved node to a larger instance size in the same instance family.

A company is creating near real-time dashboards to visualize time series data. The company ingests data into Amazon Managed Streaming for Apache Kafka (Amazon MSK). A customized data pipeline consumes the data. The pipeline then writes data to Amazon Keyspaces (for Apache Cassandra), Amazon OpenSearch Service, and Apache Avro objects in Amazon S3.
Which solution will make the data available for the data visualizations with the LEAST latency?

A. Create OpenSearch Dashboards by using the data from OpenSearch Service.

B. Use Amazon Athena with an Apache Hive metastore to query the Avro objects in Amazon S3. Use Amazon Managed Grafana to connect to Athena and to create the dashboards.

C. Use Amazon Athena to query the data from the Avro objects in Amazon S3. Configure Amazon Keyspaces as the data catalog. Connect Amazon QuickSight to Athena to create the dashboards.

D. Use AWS Glue to catalog the data. Use S3 Select to query the Avro objects in Amazon S3. Connect Amazon QuickSight to the S3 bucket to create the dashboards.

A company has used an Amazon Redshift table that is named Orders for 6 months. The company performs weekly updates and deletes on the table. The table has an interleaved sort key on a column that contains AWS Regions.
The company wants to reclaim disk space so that the company will not run out of storage space. The company also wants to analyze the sort key column.
Which Amazon Redshift command will meet these requirements?

A. VACUUM FULL Orders

B. VACUUM DELETE ONLY Orders

C. VACUUM REINDEX Orders

D. VACUUM SORT ONLY Orders

A company has implemented a lake house architecture in Amazon Redshift. The company needs to give users the ability to authenticate into Redshift query editor by using a third-party identity provider (IdP).
A data engineer must set up the authentication mechanism.
What is the first step the data engineer should take to meet this requirement?

A. Register the third-party IdP as an identity provider in the configuration settings of the Redshift cluster.

B. Register the third-party IdP as an identity provider from within Amazon Redshift.

C. Register the third-party IdP as an identity provider for AVS Secrets Manager. Configure Amazon Redshift to use Secrets Manager to manage user credentials.

D. Register the third-party IdP as an identity provider for AWS Certificate Manager (ACM). Configure Amazon Redshift to use ACM to manage user credentials.

A data engineer notices that Amazon Athena queries are held in a queue before the queries run.
How can the data engineer prevent the queries from queueing?

A. Increase the query result limit.

B. Configure provisioned capacity for an existing workgroup.

C. Use federated queries.

D. Allow users who run the Athena queries to an existing workgroup.

A company uses Amazon S3 to store semi-structured data in a transactional data lake. Some of the data files are small, but other data files are tens of terabytes.
A data engineer must perform a change data capture (CDC) operation to identify changed data from the data source. The data source sends a full snapshot as a JSON file every day and ingests the changed data into the data lake.
Which solution will capture the changed data MOST cost-effectively?

A. Create an AWS Lambda function to identify the changes between the previous data and the current data. Configure the Lambda function to ingest the changes into the data lake.

B. Ingest the data into Amazon RDS for MySQL. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.

C. Use an open source data lake format to merge the data source with the S3 data lake to insert the new data and update the existing data.

D. Ingest the data into an Amazon Aurora MySQL DB instance that runs Aurora Serverless. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.

A data engineer creates an AWS Glue Data Catalog table by using an AWS Glue crawler that is named Orders. The data engineer wants to add the following new partitions:
s3://transactions/orders/order_date=2023-01-01
s3://transactions/orders/order_date=2023-01-02
The data engineer must edit the metadata to include the new partitions in the table without scanning all the folders and files in the location of the table.
Which data definition language (DDL) statement should the data engineer use in Amazon Athena?

A. ALTER TABLE Orders ADD PARTITION(order_date=’2023-01-01’) LOCATION ‘s3://transactions/orders/order_date=2023-01-01’;ALTER TABLE Orders ADD PARTITION(order_date=’2023-01-02’) LOCATION ‘s3://transactions/orders/order_date=2023-01-02’;

B. MSCK REPAIR TABLE Orders;

C. REPAIR TABLE Orders;

D. ALTER TABLE Orders MODIFY PARTITION(order_date=’2023-01-01’) LOCATION ‘s3://transactions/orders/2023-01-01’;ALTER TABLE Orders MODIFY PARTITION(order_date=’2023-01-02’) LOCATION ‘s3://transactions/orders/2023-01-02’;

A data engineer set up an AWS Lambda function to read an object that is stored in an Amazon S3 bucket. The object is encrypted by an AWS KMS key.
The data engineer configured the Lambda function’s execution role to access the S3 bucket. However, the Lambda function encountered an error and failed to retrieve the content of the object.
What is the likely cause of the error?

A. The data engineer misconfigured the permissions of the S3 bucket. The Lambda function could not access the object.

B. The Lambda function is using an outdated SDK version, which caused the read failure.

C. The S3 bucket is located in a different AWS Region than the Region where the data engineer works. Latency issues caused the Lambda function to encounter an error.

D. The Lambda function’s execution role does not have the necessary permissions to access the KMS key that can decrypt the S3 object.

A company uses an Amazon QuickSight dashboard to monitor usage of one of the company's applications. The company uses AWS Glue jobs to process data for the dashboard. The company stores the data in a single Amazon S3 bucket. The company adds new data every day.
A data engineer discovers that dashboard queries are becoming slower over time. The data engineer determines that the root cause of the slowing queries is long-running AWS Glue jobs.
Which actions should the data engineer take to improve the performance of the AWS Glue jobs? (Choose two.)

A. Partition the data that is in the S3 bucket. Organize the data by year, month, and day.

B. Increase the AWS Glue instance size by scaling up the worker type.

C. Convert the AWS Glue schema to the DynamicFrame schema class.

D. Adjust AWS Glue job scheduling frequency so the jobs run half as many times each day.

E. Modify the IAM role that grants access to AWS glue to grant access to all S3 features.

A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format:
 
Which solution will meet this requirement with the LEAST coding effort?

A. Use AWS Glue DataBrew to read the files. Use the NEST_TO_ARRAY transformation to create the new column.

B. Use AWS Glue DataBrew to read the files. Use the NEST_TO_MAP transformation to create the new column.

C. Use AWS Glue DataBrew to read the files. Use the PIVOT transformation to create the new column.

D. Write a Lambda function in Python to read the files. Use the Python data dictionary type to create the new column.

A data engineer uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to run data pipelines in an AWS account.
A workflow recently failed to run. The data engineer needs to use Apache Airflow logs to diagnose the failure of the workflow.
Which log type should the data engineer use to diagnose the cause of the failure?

A. YourEnvironmentName-WebServer

B. YourEnvironmentName-Scheduler

C. YourEnvironmentName-DAGProcessing

D. YourEnvironmentName-Task

A telecommunications company collects network usage data throughout each day at a rate of several thousand data points each second. The company runs an application to process the usage data in real time. The company aggregates and stores the data in an Amazon Aurora DB instance.
Sudden drops in network usage usually indicate a network outage. The company must be able to identify sudden drops in network usage so the company can take immediate remedial actions.
Which solution will meet this requirement with the LEAST latency?

A. Create an AWS Lambda function to query Aurora for drops in network usage. Use Amazon EventBridge to automatically invoke the Lambda function every minute.

B. Modify the processing application to publish the data to an Amazon Kinesis data stream. Create an Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) application to detect drops in network usage.

C. Replace the Aurora database with an Amazon DynamoDB table. Create an AWS Lambda function to query the DynamoDB table for drops in network usage every minute. Use DynamoDB Accelerator (DAX) between the processing application and DynamoDB table.

D. Create an AWS Lambda function within the Database Activity Streams feature of Aurora to detect drops in network usage.

An online retail company has an application that runs on Amazon EC2 instances that are in a VPC. The company wants to collect flow logs for the VPC and analyze network traffic.
Which solution will meet these requirements MOST cost-effectively?

A. Publish flow logs to Amazon CloudWatch Logs. Use Amazon Athena for analytics.

B. Publish flow logs to Amazon CloudWatch Logs. Use an Amazon OpenSearch Service cluster for analytics.

C. Publish flow logs to Amazon S3 in text format. Use Amazon Athena for analytics.

D. Publish flow logs to Amazon S3 in Apache Parquet format. Use Amazon Athena for analytics.

A company uses a data lake that is based on an Amazon S3 bucket. To comply with regulations, the company must apply two layers of server-side encryption to files that are uploaded to the S3 bucket. The company wants to use an AWS Lambda function to apply the necessary encryption.
Which solution will meet these requirements?

A. Use both server-side encryption with AWS KMS keys (SSE-KMS) and the Amazon S3 Encryption Client.

B. Use dual-layer server-side encryption with AWS KMS keys (DSSE-KMS).

C. Use server-side encryption with customer-provided keys (SSE-C) before files are uploaded.

D. Use server-side encryption with AWS KMS keys (SSE-KMS).

A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?

A. Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.

B. Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.

C. Turn on concurrency scaling in the settings during the creation of any new Redshift cluster.

D. Turn on concurrency scaling for the daily usage quota for the Redshift cluster.

A company stores data from an application in an Amazon DynamoDB table that operates in provisioned capacity mode. The workloads of the application have predictable throughput load on a regular schedule. Every Monday, there is an immediate increase in activity early in the morning. The application has very low usage during weekends.
The company must ensure that the application performs consistently during peak usage times.
Which solution will meet these requirements in the MOST cost-effective way?

A. Increase the provisioned capacity to the maximum capacity that is currently present during peak load times.

B. Divide the table into two tables. Provision each table with half of the provisioned capacity of the original table. Spread queries evenly across both tables.

C. Use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times. Schedule lower capacity during off-peak times.

D. Change the capacity mode from provisioned to on-demand. Configure the table to scale up and scale down based on the load on the table.

A company is developing an application that runs on Amazon EC2 instances. Currently, the data that the application generates is temporary. However, the company needs to persist the data, even if the EC2 instances are terminated.
A data engineer must launch new EC2 instances from an Amazon Machine Image (AMI) and configure the instances to preserve the data.
Which solution will meet this requirement?

A. Launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume that contains the application data. Apply the default settings to the EC2 instances.

B. Launch new EC2 instances by using an AMI that is backed by a root Amazon Elastic Block Store (Amazon EBS) volume that contains the application data. Apply the default settings to the EC2 instances.

C. Launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume. Attach an Amazon Elastic Block Store (Amazon EBS) volume to contain the application data. Apply the default settings to the EC2 instances.

D. Launch new EC2 instances by using an AMI that is backed by an Amazon Elastic Block Store (Amazon EBS) volume. Attach an additional EC2 instance store volume to contain the application data. Apply the default settings to the EC2 instances.

A company plans to use Amazon Kinesis Data Firehose to store data in Amazon S3. The source data consists of 2 MB .csv files. The company must convert the .csv files to JSON format. The company must store the files in Apache Parquet format.
Which solution will meet these requirements with the LEAST development effort?

A. Use Kinesis Data Firehose to convert the .csv files to JSON. Use an AWS Lambda function to store the files in Parquet format.

B. Use Kinesis Data Firehose to convert the .csv files to JSON and to store the files in Parquet format.

C. Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON and stores the files in Parquet format.

D. Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON. Use Kinesis Data Firehose to store the files in Parquet format.

A company stores petabytes of data in thousands of Amazon S3 buckets in the S3 Standard storage class. The data supports analytics workloads that have unpredictable and variable data access patterns.
The company does not access some data for months. However, the company must be able to retrieve all data within milliseconds. The company needs to optimize S3 storage costs.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use S3 Storage Lens standard metrics to determine when to move objects to more cost-optimized storage classes. Create S3 Lifecycle policies for the S3 buckets to move objects to cost-optimized storage classes. Continue to refine the S3 Lifecycle policies in the future to optimize storage costs.

B. Use S3 Storage Lens activity metrics to identify S3 buckets that the company accesses infrequently. Configure S3 Lifecycle rules to move objects from S3 Standard to the S3 Standard-Infrequent Access (S3 Standard-IA) and S3 Glacier storage classes based on the age of the data.

C. Use S3 Intelligent-Tiering. Activate the Deep Archive Access tier.

D. Use S3 Intelligent-Tiering. Use the default access tier.

A data engineer must orchestrate a series of Amazon Athena queries that will run every day. Each query can run for more than 15 minutes.
Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)

A. Use an AWS Lambda function and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.

B. Create an AWS Step Functions workflow and add two states. Add the first state before the Lambda function. Configure the second state as a Wait state to periodically check whether the Athena query has finished using the Athena Boto3 get_query_execution API call. Configure the workflow to invoke the next query when the current query has finished running.

C. Use an AWS Glue Python shell job and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.

D. Use an AWS Glue Python shell script to run a sleep timer that checks every 5 minutes to determine whether the current Athena query has finished running successfully. Configure the Python shell script to invoke the next query when the current query has finished running.

E. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the Athena queries in AWS Batch.

A retail company has a customer data hub in an Amazon S3 bucket. Employees from many countries use the data hub to support company-wide analytics. A governance team must ensure that the company's data analysts can access data only for customers who are within the same country as the analysts.
Which solution will meet these requirements with the LEAST operational effort?

A. Create a separate table for each country’s customer data. Provide access to each analyst based on the country that the analyst serves.

B. Register the S3 bucket as a data lake location in AWS Lake Formation. Use the Lake Formation row-level security features to enforce the company’s access policies.

C. Move the data to AWS Regions that are close to the countries where the customers are. Provide access to each analyst based on the country that the analyst serves.

D. Load the data into Amazon Redshift. Create a view for each country. Create separate IAM roles for each country to provide access to data from each country. Assign the appropriate roles to the analysts.

A company needs to partition the Amazon S3 storage that the company uses for a data lake. The partitioning will use a path of the S3 object keys in the following format: s3://bucket/prefix/year=2023/month=01/day=01.
A data engineer must ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket.
Which solution will meet these requirements with the LEAST latency?

A. Schedule an AWS Glue crawler to run every morning.

B. Manually run the AWS Glue CreatePartition API twice each day.

C. Use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create_partition API call.

D. Run the MSCK REPAIR TABLE command from the AWS Glue console.