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 extracts approximately 1 TB of data every day from data sources such as SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. Some of the data sources have undefined data schemas or data schemas that change.
A data engineer must implement a solution that can detect the schema for these data sources. The solution must extract, transform, and load the data to an Amazon S3 bucket. The company has a service level agreement (SLA) to load the data into the S3 bucket within 15 minutes of data creation.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon EMR to detect the schema and to extract, transform, and load the data into the S3 bucket. Create a pipeline in Apache Spark.

B. Use AWS Glue to detect the schema and to extract, transform, and load the data into the S3 bucket. Create a pipeline in Apache Spark.

C. Create a PySpark program in AWS Lambda to extract, transform, and load the data into the S3 bucket.

D. Create a stored procedure in Amazon Redshift to detect the schema and to extract, transform, and load the data into a Redshift Spectrum table. Access the table from Amazon S3.

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 company needs to send customer call data from its on-premises PostgreSQL database to AWS to generate near real-time insights. The solution must capture and load updates from operational data stores that run in the PostgreSQL database. The data changes continuously.
A data engineer configures an AWS Database Migration Service (AWS DMS) ongoing replication task. The task reads changes in near real time from the PostgreSQL source database transaction logs for each table. The task then sends the data to an Amazon Redshift cluster for processing.
The data engineer discovers latency issues during the change data capture (CDC) of the task. The data engineer thinks that the PostgreSQL source database is causing the high latency.
Which solution will confirm that the PostgreSQL database is the source of the high latency?

A. Use Amazon CloudWatch to monitor the DMS task. Examine the CDCIncomingChanges metric to identify delays in the CDC from the source database.

B. Verify that logical replication of the source database is configured in the postgresql.conf configuration file.

C. Enable Amazon CloudWatch Logs for the DMS endpoint of the source database. Check for error messages.

D. Use Amazon CloudWatch to monitor the DMS task. Examine the CDCLatencySource metric to identify delays in the CDC from the source database.

A company stores daily records of the financial performance of investment portfolios in .csv format in an Amazon S3 bucket. A data engineer uses AWS Glue crawlers to crawl the S3 data.
The data engineer must make the S3 data accessible daily in the AWS Glue Data Catalog.
Which solution will meet these requirements?

A. Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler’s data store. Create a daily schedule to run the crawler. Configure the output destination to a new path in the existing S3 bucket.

B. Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler’s data store. Create a daily schedule to run the crawler. Specify a database name for the output.

C. Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler’s data store. Allocate data processing units (DPUs) to run the crawler every day. Specify a database name for the output.

D. Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler’s data store. Allocate data processing units (DPUs) to run the crawler every day. Configure the output destination to a new path in the existing S3 bucket.

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 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 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.

A finance company receives data from third-party data providers and stores the data as objects in an Amazon S3 bucket.
The company ran an AWS Glue crawler on the objects to create a data catalog. The AWS Glue crawler created multiple tables. However, the company expected that the crawler would create only one table.
The company needs a solution that will ensure the AVS Glue crawler creates only one table.
Which combination of solutions will meet this requirement? (Choose two.)

A. Ensure that the object format, compression type, and schema are the same for each object.

B. Ensure that the object format and schema are the same for each object. Do not enforce consistency for the compression type of each object.

C. Ensure that the schema is the same for each object. Do not enforce consistency for the file format and compression type of each object.

D. Ensure that the structure of the prefix for each S3 object name is consistent.

E. Ensure that all S3 object names follow a similar pattern.

Two developers are working on separate application releases. The developers have created feature branches named Branch A and Branch B by using a GitHub repository’s master branch as the source.
The developer for Branch A deployed code to the production system. The code for Branch B will merge into a master branch in the following week’s scheduled application release.
Which command should the developer for Branch B run before the developer raises a pull request to the master branch?

A. git diff branchB mastergit commit -m

B. git pull master

C. git rebase master

D. git fetch -b master

A data engineer is building an automated extract, transform, and load (ETL) ingestion pipeline by using AWS Glue. The pipeline ingests compressed files that are in an Amazon S3 bucket. The ingestion pipeline must support incremental data processing.
Which AWS Glue feature should the data engineer use to meet this requirement?

A. Workflows

B. Triggers

C. Job bookmarks

D. Classifiers

A data engineer must orchestrate a data pipeline that consists of one AWS Lambda function and one AWS Glue job. The solution must integrate with AWS services.
Which solution will meet these requirements with the LEAST management overhead?

A. Use an AWS Step Functions workflow that includes a state machine. Configure the state machine to run the Lambda function and then the AWS Glue job.

B. Use an Apache Airflow workflow that is deployed on an Amazon EC2 instance. Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.

C. Use an AWS Glue workflow to run the Lambda function and then the AWS Glue job.

D. Use an Apache Airflow workflow that is deployed on Amazon Elastic Kubernetes Service (Amazon EKS). Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.

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 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 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 banking company uses an application to collect large volumes of transactional data. The company uses Amazon Kinesis Data Streams for real-time analytics. The company’s application uses the PutRecord action to send data to Kinesis Data Streams.
A data engineer has observed network outages during certain times of day. The data engineer wants to configure exactly-once delivery for the entire processing pipeline.
Which solution will meet this requirement?

A. Design the application so it can remove duplicates during processing by embedding a unique ID in each record at the source.

B. Update the checkpoint configuration of the Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) data collection application to avoid duplicate processing of events.

C. Design the data source so events are not ingested into Kinesis Data Streams multiple times.

D. Stop using Kinesis Data Streams. Use Amazon EMR instead. Use Apache Flink and Apache Spark Streaming in Amazon EMR.

A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialized views.
Which solution will meet this requirement with the LEAST effort?

A. Use Apache Airflow to refresh the materialized views.

B. Use an AWS Lambda user-defined function (UDF) within Amazon Redshift to refresh the materialized views.

C. Use the query editor v2 in Amazon Redshift to refresh the materialized views.

D. Use an AWS Glue workflow to refresh the materialized views.

A company needs to build a data lake in AWS. The company must provide row-level data access and column-level data access to specific teams. The teams will access the data by using Amazon Athena, Amazon Redshift Spectrum, and Apache Hive from Amazon EMR.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon S3 for data lake storage. Use S3 access policies to restrict data access by rows and columns. Provide data access through Amazon S3.

B. Use Amazon S3 for data lake storage. Use Apache Ranger through Amazon EMR to restrict data access by rows and columns. Provide data access by using Apache Pig.

C. Use Amazon Redshift for data lake storage. Use Redshift security policies to restrict data access by rows and columns. Provide data access by using Apache Spark and Amazon Athena federated queries.

D. Use Amazon S3 for data lake storage. Use AWS Lake Formation to restrict data access by rows and columns. Provide data access through AWS Lake Formation.

A company has developed several AWS Glue extract, transform, and load (ETL) jobs to validate and transform data from Amazon S3. The ETL jobs load the data into Amazon RDS for MySQL in batches once every day. The ETL jobs use a DynamicFrame to read the S3 data.
The ETL jobs currently process all the data that is in the S3 bucket. However, the company wants the jobs to process only the daily incremental data.
Which solution will meet this requirement with the LEAST coding effort?

A. Create an ETL job that reads the S3 file status and logs the status in Amazon DynamoDB.

B. Enable job bookmarks for the ETL jobs to update the state after a run to keep track of previously processed data.

C. Enable job metrics for the ETL jobs to help keep track of processed objects in Amazon CloudWatch.

D. Configure the ETL jobs to delete processed objects from Amazon S3 after each run.

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 stores logs in an Amazon S3 bucket. When a data engineer attempts to access several log files, the data engineer discovers that some files have been unintentionally deleted.
The data engineer needs a solution that will prevent unintentional file deletion in the future.
Which solution will meet this requirement with the LEAST operational overhead?

A. Manually back up the S3 bucket on a regular basis.

B. Enable S3 Versioning for the S3 bucket.

C. Configure replication for the S3 bucket.

D. Use an Amazon S3 Glacier storage class to archive the data that is in the S3 bucket.

A data engineer needs to join data from multiple sources to perform a one-time analysis job. The data is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
Which solution will meet this requirement MOST cost-effectively?

A. Use an Amazon EMR provisioned cluster to read from all sources. Use Apache Spark to join the data and perform the analysis.

B. Copy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files.

C. Use Amazon Athena Federated Query to join the data from all data sources.

D. Use Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.

A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real-time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application.
Which solution will meet these requirements with the LEAST operational overhead?

A. Establish WebSocket connections to Amazon Redshift.

B. Use the Amazon Redshift Data API.

C. Set up Java Database Connectivity (JDBC) connections to Amazon Redshift.

D. Store frequently accessed data in Amazon S3. Use Amazon S3 Select to run the queries.

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 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 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.

During a security review, a company identified a vulnerability in an AWS Glue job. The company discovered that credentials to access an Amazon Redshift cluster were hard coded in the job script.
A data engineer must remediate the security vulnerability in the AWS Glue job. The solution must securely store the credentials.
Which combination of steps should the data engineer take to meet these requirements? (Choose two.)

A. Store the credentials in the AWS Glue job parameters.

B. Store the credentials in a configuration file that is in an Amazon S3 bucket.

C. Access the credentials from a configuration file that is in an Amazon S3 bucket by using the AWS Glue job.

D. Store the credentials in AWS Secrets Manager.

E. Grant the AWS Glue job IAM role access to the stored credentials.

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 data engineer must ingest a source of structured data that is in .csv format into an Amazon S3 data lake. The .csv files contain 15 columns. Data analysts need to run Amazon Athena queries on one or two columns of the dataset. The data analysts rarely query the entire file.
Which solution will meet these requirements MOST cost-effectively?

A. Use an AWS Glue PySpark job to ingest the source data into the data lake in .csv format.

B. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to ingest the data into the data lake in JSON format.

C. Use an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format.

D. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to write the data into the data lake in Apache Parquet format.

A company uses an Amazon Redshift provisioned cluster as its database. The Redshift cluster has five reserved ra3.4xlarge nodes and uses key distribution.
A data engineer notices that one of the nodes frequently has a CPU load over 90%. SQL Queries that run on the node are queued. The other four nodes usually have a CPU load under 15% during daily operations.
The data engineer wants to maintain the current number of compute nodes. The data engineer also wants to balance the load more evenly across all five compute nodes.
Which solution will meet these requirements?

A. Change the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.

B. Change the distribution key to the table column that has the largest dimension.

C. Upgrade the reserved node from ra3.4xlarge to ra3.16xlarge.

D. Change the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.

A data engineer finished testing an Amazon Redshift stored procedure that processes and inserts data into a table that is not mission critical. The engineer wants to automatically run the stored procedure on a daily basis.
Which solution will meet this requirement in the MOST cost-effective way?

A. Create an AWS Lambda function to schedule a cron job to run the stored procedure.

B. Schedule and run the stored procedure by using the Amazon Redshift Data API in an Amazon EC2 Spot Instance.

C. Use query editor v2 to run the stored procedure on a schedule.

D. Schedule an AWS Glue Python shell job to run the stored procedure.

A company wants to migrate an application and an on-premises Apache Kafka server to AWS. The application processes incremental updates that an on-premises Oracle database sends to the Kafka server. The company wants to use the replatform migration strategy instead of the refactor strategy.
Which solution will meet these requirements with the LEAST management overhead?

A. Amazon Kinesis Data Streams

B. Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned cluster

C. Amazon Kinesis Data Firehose

D. Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless

A company is planning to migrate on-premises Apache Hadoop clusters to Amazon EMR. The company also needs to migrate a data catalog into a persistent storage solution.
The company currently stores the data catalog in an on-premises Apache Hive metastore on the Hadoop clusters. The company requires a serverless solution to migrate the data catalog.
Which solution will meet these requirements MOST cost-effectively?

A. Use AWS Database Migration Service (AWS DMS) to migrate the Hive metastore into Amazon S3. Configure AWS Glue Data Catalog to scan Amazon S3 to produce the data catalog.

B. Configure a Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use AWS Glue Data Catalog to store the company’s data catalog as an external data catalog.

C. Configure an external Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use Amazon Aurora MySQL to store the company’s data catalog.

D. Configure a new Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use the new metastore as the company’s data catalog.

A company has multiple applications that use datasets that are stored in an Amazon S3 bucket. The company has an ecommerce application that generates a dataset that contains personally identifiable information (PII). The company has an internal analytics application that does not require access to the PII.
To comply with regulations, the company must not share PII unnecessarily. A data engineer needs to implement a solution that with redact PII dynamically, based on the needs of each application that accesses the dataset.
Which solution will meet the requirements with the LEAST operational overhead?

A. Create an S3 bucket policy to limit the access each application has. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.

B. Create an S3 Object Lambda endpoint. Use the S3 Object Lambda endpoint to read data from the S3 bucket. Implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data.

C. Use AWS Glue to transform the data for each application. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.

D. Create an API Gateway endpoint that has custom authorizers. Use the API Gateway endpoint to read data from the S3 bucket. Initiate a REST API call to dynamically redact PII based on the needs of each application that accesses the data.

A company uses an AWS Lambda function to transfer files from a legacy SFTP environment to Amazon S3 buckets. The Lambda function is VPC enabled to ensure that all communications between the Lambda function and other AVS services that are in the same VPC environment will occur over a secure network.
The Lambda function is able to connect to the SFTP environment successfully. However, when the Lambda function attempts to upload files to the S3 buckets, the Lambda function returns timeout errors. A data engineer must resolve the timeout issues in a secure way.
Which solution will meet these requirements in the MOST cost-effective way?

A. Create a NAT gateway in the public subnet of the VPC. Route network traffic to the NAT gateway.

B. Create a VPC gateway endpoint for Amazon S3. Route network traffic to the VPC gateway endpoint.

C. Create a VPC interface endpoint for Amazon S3. Route network traffic to the VPC interface endpoint.

D. Use a VPC internet gateway to connect to the internet. Route network traffic to the VPC internet gateway.

A manufacturing company wants to collect data from sensors. A data engineer needs to implement a solution that ingests sensor data in near real time.
The solution must store the data to a persistent data store. The solution must store the data in nested JSON format. The company must have the ability to query from the data store with a latency of less than 10 milliseconds.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use a self-hosted Apache Kafka cluster to capture the sensor data. Store the data in Amazon S3 for querying.

B. Use AWS Lambda to process the sensor data. Store the data in Amazon S3 for querying.

C. Use Amazon Kinesis Data Streams to capture the sensor data. Store the data in Amazon DynamoDB for querying.

D. Use Amazon Simple Queue Service (Amazon SQS) to buffer incoming sensor data. Use AWS Glue to store the data in Amazon RDS for querying.

A data engineer is processing and analyzing multiple terabytes of raw data that is in Amazon S3. The data engineer needs to clean and prepare the data. Then the data engineer needs to load the data into Amazon Redshift for analytics.
The data engineer needs a solution that will give data analysts the ability to perform complex queries. The solution must eliminate the need to perform complex extract, transform, and load (ETL) processes or to manage infrastructure.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon EMR to prepare the data. Use AWS Step Functions to load the data into Amazon Redshift. Use Amazon QuickSight to run queries.

B. Use AWS Glue DataBrew to prepare the data. Use AWS Glue to load the data into Amazon Redshift. Use Amazon Redshift to run queries.

C. Use AWS Lambda to prepare the data. Use Amazon Kinesis Data Firehose to load the data into Amazon Redshift. Use Amazon Athena to run queries.

D. Use AWS Glue to prepare the data. Use AWS Database Migration Service (AVVS DMS) to load the data into Amazon Redshift. Use Amazon Redshift Spectrum to run queries.

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 stores datasets in JSON format and .csv format in an Amazon S3 bucket. The company has Amazon RDS for Microsoft SQL Server databases, Amazon DynamoDB tables that are in provisioned capacity mode, and an Amazon Redshift cluster. A data engineering team must develop a solution that will give data scientists the ability to query all data sources by using syntax similar to SQL.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Amazon Athena to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.

B. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Redshift Spectrum to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.

C. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format. Store the transformed data in an S3 bucket. Use Amazon Athena to query the original and transformed data from the S3 bucket.

D. Use AWS Lake Formation to create a data lake. Use Lake Formation jobs to transform the data from all data sources to Apache Parquet format. Store the transformed data in an S3 bucket. Use Amazon Athena or Redshift Spectrum to query the data.

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.

A data engineer creates an AWS Lambda function that an Amazon EventBridge event will invoke. When the data engineer tries to invoke the Lambda function by using an EventBridge event, an AccessDeniedException message appears.
How should the data engineer resolve the exception?

A. Ensure that the trust policy of the Lambda function execution role allows EventBridge to assume the execution role.

B. Ensure that both the IAM role that EventBridge uses and the Lambda function’s resource-based policy have the necessary permissions.

C. Ensure that the subnet where the Lambda function is deployed is configured to be a private subnet.

D. Ensure that EventBridge schemas are valid and that the event mapping configuration is correct.

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 data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file.
Which Step Functions state should the data engineer use to meet these requirements?

A. Parallel state

B. Choice state

C. Map state

D. Wait state

A company stores details about transactions in an Amazon S3 bucket. The company wants to log all writes to the S3 bucket into another S3 bucket that is in the same AWS Region.
Which solution will meet this requirement with the LEAST operational effort?

A. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the event to Amazon Kinesis Data Firehose. Configure Kinesis Data Firehose to write the event to the logs S3 bucket.

B. Create a trail of management events in AWS CloudTraiL. Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.

C. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the events to the logs S3 bucket.

D. Create a trail of data events in AWS CloudTraiL. Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.

An ecommerce company wants to use AWS to migrate data pipelines from an on-premises environment into the AWS Cloud. The company currently uses a third-party tool in the on-premises environment to orchestrate data ingestion processes.
The company wants a migration solution that does not require the company to manage servers. The solution must be able to orchestrate Python and Bash scripts. The solution must not require the company to refactor any code.
Which solution will meet these requirements with the LEAST operational overhead?

A. AWS Lambda

B. Amazon Managed Workflows for Apache Airflow (Amazon MVVAA)

C. AWS Step Functions

D. AWS Glue

A data engineer is configuring an AWS Glue job to read data from an Amazon S3 bucket. The data engineer has set up the necessary AWS Glue connection details and an associated IAM role. However, when the data engineer attempts to run the AWS Glue job, the data engineer receives an error message that indicates that there are problems with the Amazon S3 VPC gateway endpoint.
The data engineer must resolve the error and connect the AWS Glue job to the S3 bucket.
Which solution will meet this requirement?

A. Update the AWS Glue security group to allow inbound traffic from the Amazon S3 VPC gateway endpoint.

B. Configure an S3 bucket policy to explicitly grant the AWS Glue job permissions to access the S3 bucket.

C. Review the AWS Glue job code to ensure that the AWS Glue connection details include a fully qualified domain name.

D. Verify that the VPC’s route table includes inbound and outbound routes for the Amazon S3 VPC gateway endpoint.

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 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 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 company is planning to use a provisioned Amazon EMR cluster that runs Apache Spark jobs to perform big data analysis. The company requires high reliability. A big data team must follow best practices for running cost-optimized and long-running workloads on Amazon EMR. The team must find a solution that will maintain the company's current level of performance.
Which combination of resources will meet these requirements MOST cost-effectively? (Choose two.)

A. Use Hadoop Distributed File System (HDFS) as a persistent data store.

B. Use Amazon S3 as a persistent data store.

C. Use x86-based instances for core nodes and task nodes.

D. Use Graviton instances for core nodes and task nodes.

E. Use Spot Instances for all primary nodes.