Amazon DEA-C01 Online Dumps Boost Your Career

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February 9, 2025

The Amazon DEA-C01 AWS Certified Data Engineer - Associate (DEA-C01) certification exam is challenging, and it's natural to feel anxious and nervous before taking the exam. Practicing with Amazon DEA-C01 dumps questions can help alleviate this anxiety by boosting your confidence. As you practice and become more familiar with the exam format and content, you'll feel more confident in your abilities, which can help you perform better on the actual Amazon DEA-C01 exam. Practicing with Amazon DEA-C01 questions can increase your chances of success in the certification exam. Practice free Amazon Amazon DEA-C01 exam dumps below.

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1. A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information.

The data engineer must identify and remove duplicate information from the legacy application data.

Which solution will meet these requirements with the LEAST operational overhead?

Write a custom extract, transform, and load (ETL) job in Python. Use the DataFramedrop duplicatesf) function by importing the Pandas library to perform data deduplication.

Write an AWS Glue extract, transform, and load (ETL) job. Use the FindMatches machine learning (ML) transform to transform the data to perform data deduplication.

Write a custom extract, transform, and load (ETL) job in Python. Import the Python dedupe library. Use the dedupe library to perform data deduplication.

Write an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupe library. Use the dedupe library to perform data deduplication.

2. A data engineer needs to securely transfer 5 TB of data from an on-premises data center to an Amazon S3 bucket. Approximately 5% of the data changes every day. Updates to the data need to be regularly proliferated to the S3 bucket. The data includes files that are in multiple formats. The data engineer needs to automate the transfer process and must schedule the process to run periodically.

Which AWS service should the data engineer use to transfer the data in the MOST operationally efficient way?

AWS DataSync

AWS Glue

AWS Direct Connect

Amazon S3 Transfer Acceleration

3. Files from multiple data sources arrive in an Amazon S3 bucket on a regular basis. A data engineer wants to ingest new files into Amazon Redshift in near real time when the new files arrive in the S3 bucket.

Which solution will meet these requirements?

Use the query editor v2 to schedule a COPY command to load new files into Amazon Redshift.

Use the zero-ETL integration between Amazon Aurora and Amazon Redshift to load new files into Amazon Redshift.

Use AWS Glue job bookmarks to extract, transform, and load (ETL) load new files into Amazon Redshift.

Use S3 Event Notifications to invoke an AWS Lambda function that loads new files into Amazon Redshift.

4. 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 too 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?

AWS Lambda

Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

AWS Step Functions

AWS Glue

5. A data engineer develops an AWS Glue Apache Spark ETL job to perform transformations on a dataset. When the data engineer runs the job, the job returns an error that reads, "No space left on device."

The data engineer needs to identify the source of the error and provide a solution.

Which combinations of steps will meet this requirement MOST cost-effectively? (Select TWO.)

Scale out the workers vertically to address data skewness.

Use the Spark UI and AWS Glue metrics to monitor data skew in the Spark executors.

Scale out the number of workers horizontally to address data skewness.

Enable the --write-shuffle-files-to-s3 job parameter. Use the salting technique.

Use error logs in Amazon CloudWatch to monitor data skew.

6. 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?

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.

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.

Create a PvSpark proqram in AWS Lambda to extract, transform, and load the data into the S3 bucket.

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.

7. A company has a data lake in Amazon S3. The company collects AWS CloudTrail logs for multiple applications. The company stores the logs in the data lake, catalogs the logs in AWS Glue, and partitions the logs based on the year. The company uses Amazon Athena to analyze the logs. Recently, customers reported that a query on one of the Athena tables did not return any data. A data engineer must resolve the issue.

Which combination of troubleshooting steps should the data engineer take? (Select TWO.)

A. Confirm that Athena is pointing to the correct Amazon S3 location.

B. Increase the query timeout duration.

C. Use the MSCK REPAIR TABLE command.

D. Restart Athena.

E. Delete and recreate the problematic Athena table.

8. 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?

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

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

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

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

9. A company has an application that uses a microservice architecture. The company hosts the application on an Amazon Elastic Kubernetes Services (Amazon EKS) cluster.

The company wants to set up a robust monitoring system for the application. The company needs to analyze the logs from the EKS cluster and the application. The company needs to correlate the cluster's logs with the application's traces to identify points of failure in the whole application request flow.

Which combination of steps will meet these requirements with the LEAST development effort? (Select TWO.)

Use FluentBit to collect logs. Use OpenTelemetry to collect traces.

Use Amazon CloudWatch to collect logs. Use Amazon Kinesis to collect traces.

Use Amazon CloudWatch to collect logs. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to collect traces.

Use Amazon OpenSearch to correlate the logs and traces.

Use AWS Glue to correlate the logs and traces.

10. A company currently stores all of its data in Amazon S3 by using the S3 Standard storage class.

A data engineer examined data access patterns to identify trends. During the first 6 months, most data files are accessed several times each day. Between 6 months and 2 years, most data files are accessed once or twice each month. After 2 years, data files are accessed only once or twice each year.

The data engineer needs to use an S3 Lifecycle policy to develop new data storage rules. The new storage solution must continue to provide high availability.

Which solution will meet these requirements in the MOST cost-effective way?

Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.

Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.

Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.

Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.

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11. A company receives a data file from a partner each day in an Amazon S3 bucket. The company uses a daily AW5 Glue extract, transform, and load (ETL) pipeline to clean and transform each data file. The output of the ETL pipeline is written to a CSV file named Dairy.csv in a second 53 bucket.

Occasionally, the daily data file is empty or is missing values for required fields. When the file is missing data, the company can use the previous day's CSV file.

A data engineer needs to ensure that the previous day's data file is overwritten only if the new daily file is complete and valid.

Which solution will meet these requirements with the LEAST effort?

Invoke an AWS Lambda function to check the file for missing data and to fill in missing values in required fields.

Configure the AWS Glue ETL pipeline to use AWS Glue Data Quality rules. Develop rules in Data Quality Definition Language (DQDL) to check for missing values in required files and empty files.

Use AWS Glue Studio to change the code in the ETL pipeline to fill in any missing values in the required fields with the most common values for each field.

Run a SQL query in Amazon Athena to read the CSV file and drop missing rows. Copy the corrected CSV file to the second S3 bucket.

12. 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?

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

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.

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.

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

13. A data engineer must use AWS services to ingest a dataset into an Amazon S3 data lake. The data engineer profiles the dataset and discovers that the dataset contains personally identifiable information (PII). The data engineer must implement a solution to profile the dataset and obfuscate the PII.

Which solution will meet this requirement with the LEAST operational effort?

Use an Amazon Kinesis Data Firehose delivery stream to process the dataset. Create an AWS Lambda transform function to identify the PI

Use an AWS SDK to obfuscate the PI

Set the S3 data lake as the target for the delivery stream.

Use the Detect PII transform in AWS Glue Studio to identify the PI

Obfuscate the PI

Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.

Use the Detect PII transform in AWS Glue Studio to identify the PI

Create a rule in AWS Glue Data Quality to obfuscate the PI

Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.

Ingest the dataset into Amazon DynamoD

Create an AWS Lambda function to identify and obfuscate the PII in the DynamoDB table and to transform the data. Use the same Lambda function to ingest the data into the S3 data lake.

14. A company is setting up a data pipeline in AWS. The pipeline extracts client data from Amazon S3 buckets, performs quality checks, and transforms the data. The pipeline stores the processed data in a relational database. The company will use the processed data for future queries.

Which solution will meet these requirements MOST cost-effectively?

Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue Data Quality to enforce suggested quality rules. Load the data and the quality check results into an Amazon RDS for MySQL instance.

Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data into an Amazon RDS for MySQL instance. Load the quality check results into a new S3 bucket.

Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue DataBrew to perform quality checks. Load the processed data and the quality check results into a new S3 bucket.

Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data and quality check results into an Amazon RDS for MySQL instance.

15. A data engineer wants to orchestrate a set of extract, transform, and load (ETL) jobs that run on AWS. The ETL jobs contain tasks that must run Apache Spark jobs on Amazon EMR, make API calls to Salesforce, and load data into Amazon Redshift.

The ETL jobs need to handle failures and retries automatically. The data engineer needs to use Python to orchestrate the jobs.

Which service will meet these requirements?

Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

AWS Step Functions

AWS Glue

Amazon EventBridge

16. A company maintains a data warehouse in an on-premises Oracle database. The company wants to build a data lake on AWS. The company wants to load data warehouse tables into Amazon S3 and synchronize the tables with incremental data that arrives from the data warehouse every day. Each table has a column that contains monotonically increasing values. The size of each table is less than 50 GB. The data warehouse tables are refreshed every night between 1 AM and 2 AM. A business intelligence team queries the tables between 10 AM and 8 PM every day.

Which solution will meet these requirements in the MOST operationally efficient way?

Use an AWS Database Migration Service (AWS DMS) full load plus CDC job to load tables that contain monotonically increasing data columns from the on-premises data warehouse to Amazon S3. Use custom logic in AWS Glue to append the daily incremental data to a full-load copy that is in Amazon S3.

Use an AWS Glue Java Database Connectivity (JDBC) connection. Configure a job bookmark for a column that contains monotonically increasing values. Write custom logic to append the daily incremental data to a full-load copy that is in Amazon S3.

Use an AWS Database Migration Service (AWS DMS) full load migration to load the data warehouse tables into Amazon S3 every day Overwrite the previous day's full-load copy every day.

Use AWS Glue to load a full copy of the data warehouse tables into Amazon S3 every day. Overwrite the previous day's full-load copy every day.

17. A company has a data warehouse in Amazon Redshift. To comply with security regulations, the company needs to log and store all user activities and connection activities for the data warehouse.

Which solution will meet these requirements?

Create an Amazon S3 bucket. Enable logging for the Amazon Redshift cluster. Specify the S3 bucket in the logging configuration to store the logs.

Create an Amazon Elastic File System (Amazon EFS) file system. Enable logging for the Amazon Redshift cluster. Write logs to the EFS file system.

Create an Amazon Aurora MySQL database. Enable logging for the Amazon Redshift cluster. Write the logs to a table in the Aurora MySQL database.

Create an Amazon Elastic Block Store (Amazon EBS) volume. Enable logging for the Amazon Redshift cluster. Write the logs to the EBS volume.

18. A company uses Amazon S3 as a data lake. The company sets up a data warehouse by using a multi-node Amazon Redshift cluster. The company organizes the data files in the data lake based on the data source of each data file.

The company loads all the data files into one table in the Redshift cluster by using a separate COPY command for each data file location. This approach takes a long time to load all the data files into the table. The company must increase the speed of the data ingestion. The company does not want to increase the cost of the process.

Which solution will meet these requirements?

Use a provisioned Amazon EMR cluster to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.

Load all the data files in parallel into Amazon Aurora. Run an AWS Glue job to load the data into Amazon Redshift.

Use an AWS Glue job to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.

Create a manifest file that contains the data file locations. Use a COPY command to load the data into Amazon Redshift.

19. 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.)

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

Modify the database schema to include additional tables and indexes.

Reboot the RDS DB instance once each week.

Upgrade to a larger instance size.

Implement caching to reduce the database query load.

20. 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?

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.

Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an 1AM policy that uses the tags to apply appropriate permissions to the workgroup.

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

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.

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21. A company has five offices in different AWS Regions. Each office has its own human resources (HR) department that uses a unique IAM role. The company stores employee records in a data lake that is based on Amazon S3 storage.

A data engineering team needs to limit access to the records. Each HR department should be able to access records for only employees who are within the HR department's Region.

Which combination of steps should the data engineering team take to meet this requirement with the LEAST operational overhead? (Choose two.)

A. Use data filters for each Region to register the S3 paths as data locations.

B. Register the S3 path as an AWS Lake Formation location.

C. Modify the IAM roles of the HR departments to add a data filter for each department's Region.

D. Enable fine-grained access control in AWS Lake Formation. Add a data filter for each Region.

E. Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access. Restrict access based on Region.

22. 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?

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

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

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

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

23. 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?

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

Use the streaming ingestion feature of Amazon Redshift.

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

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

24. 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

25. 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.)

Configure AWS Glue triggers to run the ETL jobs even/ hour.

Use AWS Glue DataBrewto clean and prepare the data for analytics.

Use AWS Lambda functions to schedule and run the ETL jobs even/ hour.

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

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

26. A company maintains multiple extract, transform, and load (ETL) workflows that ingest data from the company's operational databases into an Amazon S3 based data lake. The ETL workflows use AWS Glue and Amazon EMR to process data.

The company wants to improve the existing architecture to provide automated orchestration and to require minimal manual effort.

Which solution will meet these requirements with the LEAST operational overhead?

AWS Glue workflows

AWS Step Functions tasks

AWS Lambda functions

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows

27. A company stores sensitive data in an Amazon Redshift table. The company needs to give specific users the ability to access the sensitive data. The company must not create duplication in the data.

Customer support users must be able to see the last four characters of the sensitive data. Audit users must be able to see the full value of the sensitive data. No other users can have the ability to access the sensitive information.

Which solution will meet these requirements?

Create a dynamic data masking policy to allow access based on each user role. Create IAM roles that have specific access permissions. Attach the masking policy to the column that contains sensitive data.

Enable metadata security on the Redshift cluster. Create IAM users and IAM roles for the customer support users and the audit users. Grant the IAM users and IAM roles permissions to view the metadata in the Redshift cluster.

Create a row-level security policy to allow access based on each user role. Create IAM roles that have specific access permissions. Attach the security policy to the table.

Create an AWS Glue job to redact the sensitive data and to load the data into a new Redshift table.

28. A company uses Amazon Redshift as its data warehouse. Data encoding is applied to the existing tables of the data warehouse. A data engineer discovers that the compression encoding applied to some of the tables is not the best fit for the data.

The data engineer needs to improve the data encoding for the tables that have sub-optimal encoding.

Which solution will meet this requirement?

Run the ANALYZE command against the identified tables. Manually update the compression encoding of columns based on the output of the command.

Run the ANALYZE COMPRESSION command against the identified tables. Manually update the compression encoding of columns based on the output of the command.

Run the VACUUM REINDEX command against the identified tables.

Run the VACUUM RECLUSTER command against the identified tables.

29. A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket. The company ingests retail order data into the S3 bucket every day.

The company stores all order data at a single path within the S3 bucket. The data has more than 100 columns. The company ingests the order data from a third-party application that generates more than 30 files in CSV format every day. Each CSV file is between 50 and 70 MB in size.

The company uses Amazon Redshift Spectrum to run queries that select sets of columns. Users aggregate metrics based on daily orders. Recently, users have reported that the performance of the queries has degraded. A data engineer must resolve the performance issues for the queries.

Which combination of steps will meet this requirement with LEAST developmental effort? (Select TWO.)

A. Configure the third-party application to create the files in a columnar format.

B. Develop an AWS Glue ETL job to convert the multiple daily CSV files to one file for each day.

C. Partition the order data in the S3 bucket based on order date.

D. Configure the third-party application to create the files in JSON format.

E. Load the JSON data into the Amazon Redshift table in a SUPER type column.

30. A company has an application that uses an Amazon API Gateway REST API and an AWS Lambda function to retrieve data from an Amazon DynamoDB instance. Users recently reported intermittent high latency in the application's response times. A data engineer finds that the Lambda function experiences frequent throttling when the company's other Lambda functions experience increased invocations.

The company wants to ensure the API's Lambda function operates without being affected by other Lambda functions.

Which solution will meet this requirement MOST cost-effectively?

Increase the number of read capacity unit (RCU) in DynamoD

Configure provisioned concurrency for the Lambda function.

Configure reserved concurrency for the Lambda function.

Increase the Lambda function timeout and allocated memory.

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31. 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?

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

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.

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.

Load the data into Amazon Redshift. Create a view for each country. Create separate 1AM roles for each country to provide access to data from each country. Assign the appropriate roles to the analysts.

32. A company has a data pipeline that uses an Amazon RDS instance, AWS Glue jobs, and an Amazon S3 bucket. The RDS instance and AWS Glue jobs run in a private subnet of a VPC and in the same security group.

A use' made a change to the security group that prevents the AWS Glue jobs from connecting to the RDS instance. After the change, the security group contains a single rule that allows inbound SSH traffic from a specific IP address.

The company must resolve the connectivity issue.

Which solution will meet this requirement?

Add an inbound rule that allows all TCP traffic on all TCP ports. Set the security group as the source.

Add an inbound rule that allows all TCP traffic on all UDP ports. Set the private IP address of the RDS instance as the source.

Add an inbound rule that allows all TCP traffic on all TCP ports. Set the DNS name of the RDS instance as the source.

Replace the source of the existing SSH rule with the private IP address of the RDS instance. Create an outbound rule with the same source, destination, and protocol as the inbound SSH rule.

33. A company runs multiple applications on AWS. The company configured each application to output logs. The company wants to query and visualize the application logs in near real time.

Which solution will meet these requirements?

Configure the applications to output logs to Amazon CloudWatch Logs log groups. Create an Amazon S3 bucket. Create an AWS Lambda function that runs on a schedule to export the required log groups to the S3 bucket. Use Amazon Athena to query the log data in the S3 bucket.

Create an Amazon OpenSearch Service domain. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Create an OpenSearch Service subscription filter for each log group to stream the data to OpenSearch. Create the required queries and dashboards in OpenSearch Service to analyze and visualize the data.

Configure the applications to output logs to Amazon CloudWatch Logs log groups. Use CloudWatch log anomaly detection to query and visualize the log data.

Update the application code to send the log data to Amazon QuickSight by using Super-fast, Parallel, In-memory Calculation Engine (SPICE). Create the required analyses and dashboards in QuickSight.

34. 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?

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.

Configure a Hive metastore in Amazon EM

Migrate the existing on-premises Hive metastore into Amazon EM

Use AWS Glue Data Catalog to store the company's data catalog as an external data catalog.

Configure an external Hive metastore in Amazon EM

Migrate the existing on-premises Hive metastore into Amazon EM

Use Amazon Aurora MySQL to store the company's data catalog.

Configure a new Hive metastore in Amazon EM

Migrate the existing on-premises Hive metastore into Amazon EM

Use the new metastore as the company's data catalog.

35. A retail company is using an Amazon Redshift cluster to support real-time inventory management.

The company has deployed an ML model on a real-time endpoint in Amazon SageMaker.

The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs.

Which solutions will meet these requirements? (Select TWO.)

Use Amazon Redshift ML to generate inventory recommendations.

Use SQL to invoke a remote SageMaker endpoint for prediction.

Use Amazon Redshift ML to schedule regular data exports for offline model training.

Use SageMaker Autopilot to create inventory management dashboards in Amazon Redshift.

Use Amazon Redshift as a file storage system to archive old inventory management reports.

36. 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?

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

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.

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

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.

37. 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?

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.

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.

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.

Use Amazon EMR instead of AWS Glue to group the raw input files. Process the files in Amazon EM

Load the files into the Amazon Redshift tables.

38. 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?

Workflows

Triggers

Job bookmarks

Classifiers

39. A company uses AWS Glue jobs to implement several data pipelines. The pipelines are critical to the company.

The company needs to implement a monitoring mechanism that will alert stakeholders if the pipelines fail.

Which solution will meet these requirements with the LEAST operational overhead?

A. Create an Amazon EventBridge rule to match AWS Glue job failure events. Configure the rule to target an AWS Lambda function to process events. Configure the function to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.

B. Configure an Amazon CloudWatch Logs log group for the AWS Glue jobs. Create an Amazon EventBridge rule to match new log creation events in the log group. Configure the rule to target an AWS Lambda function that reads the logs and sends notifications to an Amazon Simple Notification Service (Amazon SNS) topic if AWS Glue job failure logs are present.

C. Create an Amazon EventBridge rule to match AWS Glue job failure events. Define an Amazon CloudWatch metric based on the EventBridge rule. Set up a CloudWatch alarm based on the metric to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.

D. Configure an Amazon CloudWatch Logs log group for the AWS Glue jobs. Create an Amazon EventBridge rule to match new log creation events in the log group. Configure the rule to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.

40. A company has a frontend ReactJS website that uses Amazon API Gateway to invoke REST APIs. The APIs perform the functionality of the website. A data engineer needs to write a Python script that can be occasionally invoked through API Gateway. The code must return results to API Gateway.

Which solution will meet these requirements with the LEAST operational overhead?

Deploy a custom Python script on an Amazon Elastic Container Service (Amazon ECS) cluster.

Create an AWS Lambda Python function with provisioned concurrency.

Deploy a custom Python script that can integrate with API Gateway on Amazon Elastic Kubernetes Service (Amazon EKS).

Create an AWS Lambda function. Ensure that the function is warm by scheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by using mock events.

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41. 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?

Parallel state

Choice state

Map state

Wait state

42. A company has a gaming application that stores data in Amazon DynamoDB tables. A data engineer needs to ingest the game data into an Amazon OpenSearch Service cluster. Data updates must occur in near real time.

Which solution will meet these requirements?

Use AWS Step Functions to periodically export data from the Amazon DynamoDB tables to an Amazon S3 bucket. Use an AWS Lambda function to load the data into Amazon OpenSearch Service.

Configure an AW5 Glue job to have a source of Amazon DynamoDB and a destination of Amazon OpenSearch Service to transfer data in near real time.

Use Amazon DynamoDB Streams to capture table changes. Use an AWS Lambda function to process and update the data in Amazon OpenSearch Service.

Use a custom OpenSearch plugin to sync data from the Amazon DynamoDB tables.

43. 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.

44. A company stores customer data that contains personally identifiable information (PII) in an Amazon Redshift cluster. The company's marketing, claims, and analytics teams need to be able to access the customer data.

The marketing team should have access to obfuscated claim information but should have full access to customer contact information.

The claims team should have access to customer information for each claim that the team processes.

The analytics team should have access only to obfuscated PII data.

Which solution will enforce these data access requirements with the LEAST administrative overhead?

Create a separate Redshift cluster for each team. Load only the required data for each team. Restrict access to clusters based on the teams.

Create views that include required fields for each of the data requirements. Grant the teams access only to the view that each team requires.

Create a separate Amazon Redshift database role for each team. Define masking policies that apply for each team separately. Attach appropriate masking policies to each team role.

Move the customer data to an Amazon S3 bucket. Use AWS Lake Formation to create a data lake. Use fine-grained security capabilities to grant each team appropriate permissions to access the data.

45. A data engineer is building a data pipeline. A large data file is uploaded to an Amazon S3 bucket once each day at unpredictable times. An AWS Glue workflow uses hundreds of workers to process the file and load the data into Amazon Redshift. The company wants to process the file as quickly as possible.

Which solution will meet these requirements?

Create an on-demand AWS Glue trigger to start the workflow. Create an AWS Lambda function that runs every 15 minutes to check the S3 bucket for the daily file. Configure the function to start the AWS Glue workflow if the file is present.

Create an event-based AWS Glue trigger to start the workflow. Configure Amazon S3 to log events to AWS CloudTrail. Create a rule in Amazon EventBridge to forward PutObject events to the AWS Glue trigger.

Create a scheduled AWS Glue trigger to start the workflow. Create a cron job that runs the AWS Glue job every 15 minutes. Set up the AWS Glue job to check the S3 bucket for the daily file. Configure the job to stop if the file is not present.

Create an on-demand AWS Glue trigger to start the workflow. Create an AWS Database Migration Service (AWS DMS) migration task. Set the DMS source as the S3 bucket. Set the target endpoint as the AWS Glue workflow.

46. A company uses a variety of AWS and third-party data stores. The company wants to consolidate all the data into a central data warehouse to perform analytics. Users need fast response times for analytics queries.

The company uses Amazon QuickSight in direct query mode to visualize the data. Users normally run queries during a few hours each day with unpredictable spikes.

Which solution will meet these requirements with the LEAST operational overhead?

Use Amazon Redshift Serverless to load all the data into Amazon Redshift managed storage (RMS).

Use Amazon Athena to load all the data into Amazon S3 in Apache Parquet format.

Use Amazon Redshift provisioned clusters to load all the data into Amazon Redshift managed storage (RMS).

Use Amazon Aurora PostgreSQL to load all the data into Aurora.

47. A data engineering team is using an Amazon Redshift data warehouse for operational reporting. The team wants to prevent performance issues that might result from long- running queries. A data engineer must choose a system table in Amazon Redshift to record anomalies when a query optimizer identifies conditions that might indicate performance issues.

Which table views should the data engineer use to meet this requirement?

STL USAGE CONTROL

STL ALERT EVENT LOG

STL QUERY METRICS

STL PLAN INFO

48. 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?

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.

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.

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.

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.

49. A company ingests data from multiple data sources and stores the data in an Amazon S3 bucket. An AWS Glue extract, transform, and load (ETL) job transforms the data and writes the transformed data to an Amazon S3 based data lake. The company uses Amazon Athena to query the data that is in the data lake.

The company needs to identify matching records even when the records do not have a common unique identifier.

Which solution will meet this requirement?

Use Amazon Made pattern matching as part of the ETL job.

Train and use the AWS Glue PySpark Filter class in the ETL job.

Partition tables and use the ETL job to partition the data on a unique identifier.

Train and use the AWS Lake Formation FindMatches transform in the ETL job.

50. 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?

Schedule an AWS Glue crawler to run every morning.

Manually run the AWS Glue CreatePartition API twice each day.

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

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

Page 6 of 6

51. A financial company recently added more features to its mobile app. The new features required the company to create a new topic in an existing Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster.

A few days after the company added the new topic, Amazon CloudWatch raised an alarm on the RootDiskUsed metric for the MSK cluster.

How should the company address the CloudWatch alarm?

Expand the storage of the MSK broker. Configure the MSK cluster storage to expand automatically.

Expand the storage of the Apache ZooKeeper nodes.

Update the MSK broker instance to a larger instance type. Restart the MSK cluster.

Specify the Target-Volume-in-GiB parameter for the existing topic.

52. A technology company currently uses Amazon Kinesis Data Streams to collect log data in real time. The company wants to use Amazon Redshift for downstream real-time queries and to enrich the log data.

Which solution will ingest data into Amazon Redshift with the LEAST operational overhead?

Set up an Amazon Data Firehose delivery stream to send data to a Redshift provisioned cluster table.

Set up an Amazon Data Firehose delivery stream to send data to Amazon S3. Configure a Redshift provisioned cluster to load data every minute.

Configure Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to send data directly to a Redshift provisioned cluster table.

Use Amazon Redshift streaming ingestion from Kinesis Data Streams and to present data as a materialized view.

53. A company is building an analytics solution. The solution uses Amazon S3 for data lake storage and Amazon Redshift for a data warehouse. The company wants to use Amazon Redshift Spectrum to query the data that is in Amazon S3.

Which actions will provide the FASTEST queries? (Choose two.)

Use gzip compression to compress individual files to sizes that are between 1 GB and 5 G

Use a columnar storage file format.

Partition the data based on the most common query predicates.

Split the data into files that are less than 10 K

Use file formats that are not

54. 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?

AWS Glue

Amazon EMR

AWS Lambda

Amazon Redshift

55. A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary.

The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to use Data Quality Definition Language (DQDL) rules in an AWS Glue Data Quality job.

Which rule will meet these requirements?

DatasetMatch "reference" "city->ref_city, state->ref_state" = 1.0

ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 1.0

DatasetMatch "reference" "city->ref_city, state->ref_state" = 100

ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 100

56. 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?

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.

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.

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.

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.

57. 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?

Amazon Managed Streaming for Apache Kafka (Amazon MSK)

Amazon AppFlow

AWS Glue Data Catalog

Amazon Kinesis

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