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 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 needs Amazon Athena queries to finish faster. The data engineer notices that all the files the Athena queries use are currently stored in uncompressed .csv format. The data engineer also notices that users perform most queries by selecting a specific column.
Which solution will MOST speed up the Athena query performance?

A. Change the data format from .csv to JSON format. Apply Snappy compression.

B. Compress the .csv files by using Snappy compression.

C. Change the data format from .csv to Apache Parquet. Apply Snappy compression.

D. Compress the .csv files by using gzip compression.

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 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 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 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.

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 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 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 data engineer must manage the ingestion of real-time streaming data into AWS. The data engineer wants to perform real-time analytics on the incoming streaming data by using time-based aggregations over a window of up to 30 minutes. The data engineer needs a solution that is highly fault tolerant.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use an AWS Lambda function that includes both the business and the analytics logic to perform time-based aggregations over a window of up to 30 minutes for the data in Amazon Kinesis Data Streams.

B. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data that might occasionally contain duplicates by using multiple types of aggregations.

C. Use an AWS Lambda function that includes both the business and the analytics logic to perform aggregations for a tumbling window of up to 30 minutes, based on the event timestamp.

D. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data by using multiple types of aggregations to perform time-based analytics over a window of up to 30 minutes.

A company stores customer data tables that include customer addresses in an AWS Lake Formation data lake. To comply with new regulations, the company must ensure that users cannot access data for customers who are in Canada.
The company needs a solution that will prevent user access to rows for customers who are in Canada.
Which solution will meet this requirement with the LEAST operational effort?

A. Set a row-level filter to prevent user access to a row where the country is Canada.

B. Create an IAM role that restricts user access to an address where the country is Canada.

C. Set a column-level filter to prevent user access to a row where the country is Canada.

D. Apply a tag to all rows where Canada is the country. Prevent user access where the tag is equal to “Canada”.

A financial company wants to use Amazon Athena to run on-demand SQL queries on a petabyte-scale dataset to support a business intelligence (BI) application. An AWS Glue job that runs during non-business hours updates the dataset once every day. The BI application has a standard data refresh frequency of 1 hour to comply with company policies.
A data engineer wants to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs.
Which solution will meet these requirements with the LEAST operational overhead?

A. Configure an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archive storage class after 1 day.

B. Use the query result reuse feature of Amazon Athena for the SQL queries.

C. Add an Amazon ElastiCache cluster between the BI application and Athena.

D. Change the format of the files that are in the dataset to Apache Parquet.

A data engineer must build an extract, transform, and load (ETL) pipeline to process and load data from 10 source systems into 10 tables that are in an Amazon Redshift database. All the source systems generate .csv, JSON, or Apache Parquet files every 15 minutes. The source systems all deliver files into one Amazon S3 bucket. The file sizes range from 10 MB to 20 GB. The ETL pipeline must function correctly despite changes to the data schema.
Which data pipeline solutions will meet these requirements? (Choose two.)

A. Use an Amazon EventBridge rule to run an AWS Glue job every 15 minutes. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.

B. Use an Amazon EventBridge rule to invoke an AWS Glue workflow job every 15 minutes. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.

C. Configure an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket. Configure an AWS Glue job to process and load the data into the Amazon Redshift tables. Create a second Lambda function to run the AWS Glue job. Create an Amazon EventBridge rule to invoke the second Lambda function when the AWS Glue crawler finishes running successfully.

D. Configure an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.

E. Configure an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket. Configure the AWS Glue job to read the files from the S3 bucket into an Apache Spark DataFrame. Configure the AWS Glue job to also put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to load data into the Amazon Redshift tables.

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 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 is using an AWS Glue crawler to catalog data that is in an Amazon S3 bucket. The S3 bucket contains both .csv and json files. The data engineer configured the crawler to exclude the .json files from the catalog.
When the data engineer runs queries in Amazon Athena, the queries also process the excluded .json files. The data engineer wants to resolve this issue. The data engineer needs a solution that will not affect access requirements for the .csv files in the source S3 bucket.
Which solution will meet this requirement with the SHORTEST query times?

A. Adjust the AWS Glue crawler settings to ensure that the AWS Glue crawler also excludes .json files.

B. Use the Athena console to ensure the Athena queries also exclude the .json files.

C. Relocate the .json files to a different path within the S3 bucket.

D. Use S3 bucket policies to block access to the .json files.

A security company stores IoT data that is in JSON format in an Amazon S3 bucket. The data structure can change when the company upgrades the IoT devices. The company wants to create a data catalog that includes the IoT data. The company's analytics department will use the data catalog to index the data.
Which solution will meet these requirements MOST cost-effectively?

A. Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create a new AWS Glue workload to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.

B. Create an Amazon Redshift provisioned cluster. Create an Amazon Redshift Spectrum database for the analytics department to explore the data that is in Amazon S3. Create Redshift stored procedures to load the data into Amazon Redshift.

C. Create an Amazon Athena workgroup. Explore the data that is in Amazon S3 by using Apache Spark through Athena. Provide the Athena workgroup schema and tables to the analytics department.

D. Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create AWS Lambda user defined functions (UDFs) by using the Amazon Redshift Data API. Create an AWS Step Functions job to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.

A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records.
A data engineer needs to find a solution to process the streaming data. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data.
Which solution will meet these requirements with the LEAST operational overhead?

A. Load data into Amazon Kinesis Data Firehose. Load the data into Amazon Redshift.

B. Use the streaming ingestion feature of Amazon Redshift.

C. Load the data into Amazon S3. Use the COPY command to load the data into Amazon Redshift.

D. Use the Amazon Aurora zero-ETL integration with Amazon Redshift.

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 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 company uploads .csv files to an Amazon S3 bucket. The company’s data platform team has set up an AWS Glue crawler to perform data discovery and to create the tables and schemas.
An AWS Glue job writes processed data from the tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creates the Amazon Redshift tables in the Redshift database appropriately.
If the company reruns the AWS Glue job for any reason, duplicate records are introduced into the Amazon Redshift tables. The company needs a solution that will update the Redshift tables without duplicates.
Which solution will meet these requirements?

A. Modify the AWS Glue job to copy the rows into a staging Redshift table. Add SQL commands to update the existing rows with new values from the staging Redshift table.

B. Modify the AWS Glue job to load the previously inserted data into a MySQL database. Perform an upsert operation in the MySQL database. Copy the results to the Amazon Redshift tables.

C. Use Apache Spark’s DataFrame dropDuplicates() API to eliminate duplicates. Write the data to the Redshift tables.

D. Use the AWS Glue ResolveChoice built-in transform to select the value of the column from the most recent record.

A company is migrating on-premises workloads to AWS. The company wants to reduce overall operational overhead. The company also wants to explore serverless options.
The company's current workloads use Apache Pig, Apache Oozie, Apache Spark, Apache Hbase, and Apache Flink. The on-premises workloads process petabytes of data in seconds. The company must maintain similar or better performance after the migration to AWS.
Which extract, transform, and load (ETL) service will meet these requirements?

A. AWS Glue

B. Amazon EMR

C. AWS Lambda

D. Amazon Redshift

A company needs to set up a data catalog and metadata management for data sources that run in the AWS Cloud. The company will use the data catalog to maintain the metadata of all the objects that are in a set of data stores. The data stores include structured sources such as Amazon RDS and Amazon Redshift. The data stores also include semistructured sources such as JSON files and .xml files that are stored in Amazon S3.
The company needs a solution that will update the data catalog on a regular basis. The solution also must detect changes to the source metadata.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically.

B. Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and to update the Data Catalog with metadata changes. Schedule the crawlers to run periodically to update the metadata catalog.

C. Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically.

D. Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog.

A data engineer uses Amazon Redshift to run resource-intensive analytics processes once every month. Every month, the data engineer creates a new Redshift provisioned cluster. The data engineer deletes the Redshift provisioned cluster after the analytics processes are complete every month. Before the data engineer deletes the cluster each month, the data engineer unloads backup data from the cluster to an Amazon S3 bucket.
The data engineer needs a solution to run the monthly analytics processes that does not require the data engineer to manage the infrastructure manually.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month.

B. Use Amazon Redshift Serverless to automatically process the analytics workload.

C. Use the AWS CLI to automatically process the analytics workload.

D. Use AWS CloudFormation templates to automatically process the analytics workload.

A retail company uses AWS Glue for extract, transform, and load (ETL) operations on a dataset that contains information about customer orders. The company wants to implement specific validation rules to ensure data accuracy and consistency.
Which solution will meet these requirements?

A. Use AWS Glue job bookmarks to track the data for accuracy and consistency.

B. Create custom AWS Glue Data Quality rulesets to define specific data quality checks.

C. Use the built-in AWS Glue Data Quality transforms for standard data quality validations.

D. Use AWS Glue Data Catalog to maintain a centralized data schema and metadata repository.

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 is building a data pipeline on AWS by using AWS Glue extract, transform, and load (ETL) jobs. The data engineer needs to process data from Amazon RDS and MongoDB, perform transformations, and load the transformed data into Amazon Redshift for analytics. The data updates must occur every hour.
Which combination of tasks will meet these requirements with the LEAST operational overhead? (Choose two.)

A. Configure AWS Glue triggers to run the ETL jobs every hour.

B. Use AWS Glue DataBrew to clean and prepare the data for analytics.

C. Use AWS Lambda functions to schedule and run the ETL jobs every hour.

D. Use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift.

E. Use the Redshift Data API to load transformed data into Amazon Redshift.

A company uses Apache Airflow to orchestrate the company's current on-premises data pipelines. The company runs SQL data quality check tasks as part of the pipelines. The company wants to migrate the pipelines to AWS and to use AWS managed services.
Which solution will meet these requirements with the LEAST amount of refactoring?

A. Setup AWS Outposts in the AWS Region that is nearest to the location where the company uses Airflow. Migrate the servers into Outposts hosted Amazon EC2 instances. Update the pipelines to interact with the Outposts hosted EC2 instances instead of the on-premises pipelines.

B. Create a custom Amazon Machine Image (AMI) that contains the Airflow application and the code that the company needs to migrate. Use the custom AMI to deploy Amazon EC2 instances. Update the network connections to interact with the newly deployed EC2 instances.

C. Migrate the existing Airflow orchestration configuration into Amazon Managed Workflows for Apache Airflow (Amazon MWAA). Create the data quality checks during the ingestion to validate the data quality by using SQL tasks in Airflow.

D. Convert the pipelines to AWS Step Functions workflows. Recreate the data quality checks in SQL as Python based AWS Lambda functions.

A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data.
Which solution will meet these requirements with the LEAST operational overhead?

A. Configure an AWS Lambda function to load data from the S3 bucket into a pandas dataframe. Write a SQL SELECT statement on the dataframe to query the required column.

B. Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects.

C. Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column.

D. Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in Amazon Athena to query the required column.

A company uses Amazon EMR as an extract, transform, and load (ETL) pipeline to transform data that comes from multiple sources. A data engineer must orchestrate the pipeline to maximize performance.
Which AWS service will meet this requirement MOST cost effectively?

A. Amazon EventBridge

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

C. AWS Step Functions

D. AWS Glue Workflows

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 data engineer needs to schedule a workflow that runs a set of AWS Glue jobs every day. The data engineer does not require the Glue jobs to run or finish at a specific time.
Which solution will run the Glue jobs in the MOST cost-effective way?

A. Choose the FLEX execution class in the Glue job properties.

B. Use the Spot Instance type in Glue job properties.

C. Choose the STANDARD execution class in the Glue job properties.

D. Choose the latest version in the GlueVersion field in the Glue job properties.

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 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 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 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 data engineer has implemented data quality rules in 1,000 AWS Glue Data Catalog tables. Because of a recent change in business requirements, the data engineer must edit the data quality rules.
How should the data engineer meet this requirement with the LEAST operational overhead?

A. Create a pipeline in AWS Glue ETL to edit the rules for each of the 1,000 Data Catalog tables. Use an AWS Lambda function to call the corresponding AWS Glue job for each Data Catalog table.

B. Create an AWS Lambda function that makes an API call to AWS Glue Data Quality to make the edits.

C. Create an Amazon EMR cluster. Run a pipeline on Amazon EMR that edits the rules for each Data Catalog table. Use an AWS Lambda function to run the EMR pipeline.

D. Use the AWS Management Console to edit the rules within the Data Catalog.

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 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

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 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 is planning to upgrade its Amazon Elastic Block Store (Amazon EBS) General Purpose SSD storage from gp2 to gp3. The company wants to prevent any interruptions in its Amazon EC2 instances that will cause data loss during the migration to the upgraded storage.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create snapshots of the gp2 volumes. Create new gp3 volumes from the snapshots. Attach the new gp3 volumes to the EC2 instances.

B. Create new gp3 volumes. Gradually transfer the data to the new gp3 volumes. When the transfer is complete, mount the new gp3 volumes to the EC2 instances to replace the gp2 volumes.

C. Change the volume type of the existing gp2 volumes to gp3. Enter new values for volume size, IOPS, and throughput.

D. Use AWS DataSync to create new gp3 volumes. Transfer the data from the original gp2 volumes to the new gp3 volumes.

A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded.
A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB.
How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?

A. Use a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events.

B. Use the Amazon Redshift Data API to publish an event to Amazon EventBridge. Configure an EventBridge rule to invoke the Lambda function.

C. Use the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke the Lambda function.

D. Use a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events.

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 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 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.

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 marketing company collects clickstream data. The company sends the clickstream data to Amazon Kinesis Data Firehose and stores the clickstream data in Amazon S3. The company wants to build a series of dashboards that hundreds of users from multiple departments will use.
The company will use Amazon QuickSight to develop the dashboards. The company wants a solution that can scale and provide daily updates about clickstream activity.
Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)

A. Use Amazon Redshift to store and query the clickstream data.

B. Use Amazon Athena to query the clickstream data

C. Use Amazon S3 analytics to query the clickstream data.

D. Access the query data through a QuickSight direct SQL query.

E. Access the query data through QuickSight SPICE (Super-fast, Parallel, In-memory Calculation Engine). Configure a daily refresh for the dataset.

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