AWS Data Engineer Associate - Test 16

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Welcome to your AWS Data Engineer Associate – Test-16

Name

Phone

Q1. You are designing a recommendation engine for an e-commerce platform that needs to analyze the relationships between products and user interactions to make personalized product recommendations. Which data structure is most suitable for representing and analyzing these complex relationships?

Select 1 option(s):

Arrays

Stacks

Graphs

Hash Tables

Q2. An organization needs to manage and rotate credentials used by its EC2 instances to access a database securely. They require a solution that integrates with AWS Identity and Access Management (IAM) for access control. Which AWS service should they use for this purpose?

Select 1 option(s):

Implement AWS Secrets Manager to manage and rotate the credentials with IAM integration.

Use AWS Systems Manager Parameter Store with standard parameters for storing database credentials.

Store the credentials in an Amazon DynamoDB table and manage access through IAM roles.

Keep the credentials in Amazon Elastic Block Store (EBS) volumes attached to the EC2 instances.

Q3. You are working with a large dataset, and a complex SQL query is taking a significant amount of time to complete. After reviewing the query execution plan, you notice that there are multiple nested subqueries. What is a recommended strategy to optimize the query?

Select 1 option(s):

Increase the complexity of subqueries

Rewrite subqueries as separate queries

Add more nested subqueries

Increase the query‘s response time

Q4. When preparing a dataset for machine learning in Amazon SageMaker Data Wrangler, a data scientist discovers that some categorical data is inconsistently formatted. What should the scientist do within Data Wrangler to standardize these categorical values?

Select 1 option(s):

Use Amazon Athena integration in Data Wrangler to run SQL queries for data standardization.

Implement Python scripting in Data Wrangler to manually correct the categorical data formats.

Apply Data Wrangler‘s data transformation capabilities to unify the format of the categorical data.

Configure an AWS Lambda function to preprocess and format the data before importing it into Data Wrangler.

Q5. A data engineering team is concerned about securing sensitive data stored in their Amazon S3 buckets. They want to automatically identify and classify sensitive information like personally identifiable information (PII). Which AWS service should they use for this purpose?

Select 1 option(s):

Implement AWS Shield for advanced protection against data breaches.

Use Amazon Inspector to assess the security of the data stored in S3.

Configure Amazon Macie to automatically discover and protect sensitive data in S3.

Enable Amazon GuardDuty for continuous monitoring and malicious activity detection.

Q6. A data engineering team wants to audit and monitor all data-related activities within their AWS environment, including changes to data resources and access to sensitive data. Which AWS service should they primarily use to achieve comprehensive logging of these activities?

Select 1 option(s):

Implement Amazon CloudWatch for real-time monitoring of data resource metrics.

Use AWS Config to track changes in the configurations of AWS resources.

Configure AWS CloudTrail to log and monitor actions taken on AWS services and resources.

Utilize Amazon Inspector for automated security assessment of AWS resources.

Q7. A development team is building a serverless application on AWS that interacts with an Amazon RDS database. They need a secure and efficient way to store and retrieve the database credentials. Which AWS service is most suitable for storing these database credentials?

Select 1 option(s):

Use AWS Systems Manager Parameter Store to securely store and manage database credentials.

Store the credentials as environment variables in the AWS Lambda function.

Keep the database credentials in an Amazon S3 bucket with server-side encryption.

Hardcode the database credentials within the application code for direct access.

Q8. A company uses Amazon Redshift for its data warehousing needs. They are concerned about protecting their data, both at rest and in transit. Which of the following options should they choose to ensure their data in Amazon Redshift is encrypted?

Select 1 option(s):

A company uses Amazon Redshift for its data warehousing needs

Configure Amazon Redshift to use AWS Key Management Service (KMS) for data encryption at rest and enable SSL/TLS for data in transit.

Use Amazon S3 server-side encryption for data at rest and Network Access Control Lists (NACLs) for data in transit.

Implement Amazon Cognito user pools for encrypting data at rest and data in transit.

Q9. You are responsible for optimizing SQL queries in a database-driven application. One of the queries in your application is running slowly, affecting overall performance. After analyzing the query execution plan, you discover that there is a lack of appropriate indexes. Which of the following actions should you take to improve the query performance?

Select 1 option(s):

Increase the query complexity

Add additional sorting operations

Create relevant indexes

Increase the batch size

Q10. A data engineer is tasked with ensuring that all objects in an Amazon S3 bucket are automatically moved to the Amazon S3 Glacier storage class after 30 days of inactivity. Additionally, they want to ensure that the objects are permanently deleted after 365 days of inactivity. Which configuration should the data engineer use to achieve this?

Select 1 option(s):

Configure an S3 bucket policy to move objects to Glacier after 30 days and set a lifecycle policy to delete objects after 365 days.

Configure an S3 bucket policy to move objects to Glacier after 30 days and use versioning to delete objects after 365 days.

Configure an S3 bucket policy to move objects to Glacier after 30 days and use object tagging to delete objects after 365 days.

Configure an S3 bucket policy to move objects to Glacier after 30 days and use Amazon S3 Object Lock to delete objects after 365 days.

Q11. A financial institution needs to share transactional data with a third-party analytics service but must adhere to GDPR regulations by not exposing any customers personal data. What method should they use to prepare this data for sharing?

Select 1 option(s):

Transfer the data using AWS DataSync to ensure secure transfer to the third party.

Apply data anonymization techniques to remove or obscure personal identifiers in the transactional data.

Encrypt the data using Amazon S3 server-side encryption before sharing it with the third party.

Use AWS IAM roles to control access to the data, ensuring only the third party can access it.

Q12. A company stores its sales data in JSON format in Amazon S3. The data engineering team needs to use Amazon Athena to run ad-hoc queries on this data. They want to create a structured view of the data for easier querying. What is the first step the team should take in Athena?

Select 1 option(s):

Execute a CREATE DATABASE command in Athena to store the structured view.

Use a CREATE EXTERNAL TABLE command in Athena to define the schema of the JSON data.

Implement an AWS Glue crawler to convert the JSON data to a CSV format for Athena.

Configure an AWS Data Pipeline job to transform and load the data into Amazon Redshift for querying.

Q13. A data engineering team is deploying a new application on AWS and wants to ensure that all operational logs are centrally collected and easily accessible for analysis. Which AWS service should they primarily use for deploying and managing these application logs?

Select 1 option(s):

Configure Amazon S3 to store application logs and set up lifecycle policies for log retention.

Use Amazon CloudWatch Logs to collect, monitor, and analyze application logs.

Implement Amazon Redshift to aggregate and analyze the application logs.

Deploy AWS Lambda functions to process and store logs in Amazon DynamoDB.

Q14. You are designing a compute solution for a workload that requires running large-scale batch processing jobs with high performance and scalability. The workload involves processing large datasets in parallel and requires automatic scaling based on job demand. Which AWS compute service is the most appropriate choice for this scenario?

Select 1 option(s):

AWS Batch

Amazon EMR

AWS Lambda

AWS Fargate

Q15. A company is using Amazon Redshift for their data warehousing needs. They have a requirement to grant different levels of access to various teams: the data engineering team needs full access to all tables, while the analytics team only requires read access. What is the most effective method to set up these access controls in Amazon Redshift?

Select 1 option(s):

Configure Security Groups in Redshift to control access at the network level for different teams.

Use Redshift‘s IAM integration to assign IAM roles to each team with the required table-level permissions.

Create Redshift user groups for each team and assign appropriate permissions to these groups using GRANT statements.

Q16. A team is using AWS Glue for ETL processes. They have noticed that some jobs are taking longer than expected. What action can the team take to improve the performance of their AWS Glue jobs?

Select 1 option(s):

Increase the data processing units (DPUs) allocated to the Glue jobs.

Migrate the ETL processes to Amazon EC2 instances for better control over resources.

Store the data in Amazon RDS instead of S3 to speed up the ETL processes.

Use AWS Lambda instead of Glue for more efficient data processing.

Q17. A financial institution needs to delete customer transaction records that are older than seven years to comply with data retention laws. What is the most appropriate AWS service or feature to implement this data deletion strategy?

Select 1 option(s):

Configure Amazon S3 lifecycle policies to delete objects older than seven years.

Use Amazon Redshift Spectrum to archive old records to a separate schema.

Write a custom script to delete records from Amazon RDS based on their age.

Enable Amazon Glacier to automatically delete objects after seven years.

Q18. A healthcare organization is migrating its patient records to AWS and needs to comply with HIPAA regulations regarding patient data. They want to analyze the data without exposing sensitive patient information. What approach should they take to ensure compliance while enabling data analysis?

Select 1 option(s):

Encrypt the entire dataset with AWS KMS and grant access only to authorized personnel.

Implement data masking techniques to anonymize patient-specific information before analysis.

Store all patient records in an Amazon S3 bucket with server-side encryption enabled.

Use Amazon Redshift Spectrum to query the data, relying on its built-in security features for compliance.

Q19. The compliance officer of a company wants to monitor and report on the storage and usage of sensitive data within their AWS environment to adhere to regulatory standards. They need a service that can provide detailed reports on where sensitive data is stored and how its being used. Which AWS service is most suitable for these requirements?

Select 1 option(s):

Use AWS Config to track changes in AWS resource configurations and compliance.

Implement Amazon Macie for continuous monitoring of sensitive data and generating detailed reports.

Configure AWS CloudTrail to log and monitor actions taken on AWS services.

Apply AWS Trusted Advisor for best practices and optimization recommendations.

Q20. An organization is using Athena notebooks with Apache Spark to analyze real-time streaming data from IoT devices. The data is ingested into Amazon Kinesis and needs to be explored in the notebook. Which approach should the data engineer take to analyze this streaming data in the notebook?

Select 1 option(s):

Use Spark Structured Streaming in the notebook to read data directly from Amazon Kinesis.

First transfer the streaming data from Kinesis to Amazon S3, then use Spark SQL to query the data.

Implement an AWS Lambda function to process and store the data in Amazon RDS, then query it using Spark.

Configure Amazon QuickSight to visualize the streaming data and then import the visualizations into the notebook.

Q21. A data engineer is designing a data storage solution using Amazon DynamoDB. They need to automatically delete items from a DynamoDB table when they reach a certain age. Which feature of DynamoDB should they configure to achieve this?

Select 1 option(s):

DynamoDB Conditional Writes

DynamoDB Global Tables

DynamoDB Point-in-Time Recovery

DynamoDB Time To Live (TTL)

Q22. A company wants to ensure that their marketing team can query only marketing-related data using Amazon Athena, while the data is governed and secured by AWS Lake Formation. What is the best approach to achieve this?

Select 1 option(s):

Implement Network Access Control Lists (NACLs) to restrict Athena query access to marketing data.

Use Athena‘s built-in user management system to grant the marketing team access to specific datasets.

Apply Lake Formation permissions to give the marketing team query access only to the marketing-related datasets.

Store marketing data in a dedicated Amazon RDS instance and integrate it with Athena for querying.

Q23. A healthcare organization needs to delete patient medical records that have reached their mandated retention period of ten years. These records are stored in Amazon RDS. What is the recommended approach to delete this data securely and efficiently?

Select 1 option(s):

Run manual SQL queries to delete records older than ten years from the RDS database.

Configure Amazon RDS automatic data retention policies to delete records older than ten years.

Export the data to Amazon S3 and use S3 lifecycle policies to delete objects older than ten years.

Create an Amazon Redshift cluster and transfer old records for later deletion.

Q24. A media company wants to create a data catalog for their video and audio content stored in Amazon S3 and their metadata stored in Amazon RDS. They need to establish connections to both these data sources for cataloging purposes. Which AWS service should they use to create these new source or target connections?

Select 1 option(s):

Amazon Athena

AWS Glue

Amazon Redshift

AWS Data Pipeline

Q25. A healthcare organization is using Amazon S3 to store patient records and has integrated AWS Lake Formation for managing data access. They need to ensure that only authorized research teams can access specific subsets of patient data for analysis. What is the most effective way to manage these permissions using Lake Formation?

Select 1 option(s):

Configure Amazon S3 bucket policies to grant access to authorized research teams.

Use AWS IAM roles to assign specific access rights to the research teams for the relevant S3 buckets.

Apply Lake Formation permissions to give fine-grained access control to the research teams for the specific datasets in S3.

Implement Network Access Control Lists (NACLs) for the S3 buckets to restrict access to the research teams.

Q26. A media streaming company relies on Amazon S3 to store its video content. They want to protect their data against accidental deletions and require versioning to be enabled. Which option should the company choose to meet these requirements?

Select 1 option(s):

Use Amazon S3 Cross-Region Replication to replicate the data to another region.

Enable Amazon S3 Object Lock to prevent accidental deletions.

Use AWS Identity and Access Management (IAM) policies to restrict access.

Create a new Amazon S3 bucket for each video for better isolation.

Q27. A data engineering team is evaluating encryption options for their new Amazon Redshift cluster that will store sensitive customer data. They need to comply with regulatory requirements for encryption of data at rest. What should they implement to meet this requirement?

Select 1 option(s):

A data engineering team is evaluating encryption options for their new

Enable Amazon Redshift‘s automatic encryption feature using either AWS KMS or Amazon S3 managed keys.

Encrypt the data at the application level before loading it into the Redshift cluster.

Apply Amazon RDS encryption features to the Redshift cluster for data at rest.

Q28. A financial institution is utilizing Amazon S3 for storing transaction data and wants to implement granular access control to ensure that only certain analysts can access transaction data from the previous fiscal year. They are using AWS Lake Formation for data governance. How should they configure access to meet this requirement?

Select 1 option(s):

Use Amazon S3 lifecycle policies to manage access to the transaction data based on its age.

Create IAM policies with conditions based on the object creation date in S3 to restrict access.

Set up Lake Formation permissions to control access to the specific transaction data based on the time frame.

Implement Amazon S3 versioning and grant access to specific versions corresponding to the previous fiscal year.

Q29. An organization is using multiple AWS services for their application, including Amazon EC2, RDS, and Lambda. They need a centralized solution to manage and rotate credentials used by these services. What is the most suitable AWS service for this requirement?

Select 1 option(s):

Implement AWS Key Management Service (KMS) for centralized credential management.

Utilize AWS Secrets Manager for storing, managing, and rotating credentials for all services.

Store credentials in Amazon DynamoDB and manually manage rotation.

Use Amazon S3 with lifecycle policies to store and update credentials periodically.

Q30. A company has two AWS accounts: Account A for development and Account B for production. They want to ensure that data encrypted in Account A using AWS Key Management Service (KMS) can be securely accessed by services in Account B. What is the best approach to configure this cross-account access?

Select 1 option(s):

Store the encrypted data in a shared Amazon S3 bucket and use S3 bucket policies to grant access to Account B.

Use AWS IAM cross-account roles to allow services in Account B to use the KMS key in Account A.

Replicate the KMS key from Account A to Account B to enable access to the encrypted data.

Implement Network Access Control Lists (NACLs) to allow data transfer between Account A and Account B.

Q31. An organization utilizes Amazon Redshift for storing sensitive financial data. They need to ensure that only specific database administrators can modify the data, while other team members can only run queries. What is the appropriate authorization mechanism to implement this in Amazon Redshift?

Select 1 option(s):

Set up Amazon Redshift Spectrum for different access levels to the data based on the team roles.

Apply Network Access Control Lists (NACLs) to restrict database modification rights to certain IP addresses.

Create separate database user accounts with tailored GRANT permissions based on team member roles.

Configure AWS Secrets Manager to control access to the Redshift database based on user roles.

Q32. An organization is using AWS Lambda functions as part of their data processing pipeline. They want to be immediately alerted if any Lambda function runs into errors or exceeds execution time thresholds. Which AWS services should they use to achieve this?

Select 1 option(s):

Enable AWS X-Ray for the Lambda functions and configure alerts with Amazon QuickSight.

Configure CloudWatch Logs for the Lambda functions and set up alarms with Amazon SNS for notifications.

Utilize AWS Step Functions to monitor Lambda executions and send alerts via AWS SES.

Implement AWS Glue job monitoring for Lambda functions and integrate with Amazon Chime for alerts.

Q33. A company has recently integrated multiple data sources into their AWS environment, including data stored in Amazon S3, Amazon RDS, and Amazon DynamoDB. They need to automatically discover the schema of these diverse data sets and make this information available for ETL processes. Which AWS service should they use to populate their data catalog with this schema information?

Select 1 option(s):

AWS Glue Crawlers

Amazon Athena

AWS Data Pipeline

Amazon Redshift Spectrum

Q34. An educational institution uses Amazon EMR for research data analysis and manages data access through AWS Lake Formation. They need to ensure that researchers can query only the data relevant to their specific research projects. What is the recommended way to manage these permissions in Lake Formation?

Select 1 option(s):

Configure EMR security configurations to restrict data access based on researcher roles.

Apply IAM role-based access controls to the EMR clusters for different research projects.

Utilize Lake Formation‘s granular access controls to assign data permissions based on research project requirements.

Implement Amazon Cognito user pools to manage access permissions for researchers on EMR.

Q35. A data engineer notices that an AWS Glue ETL job is taking significantly longer to complete than expected. The job processes a large dataset stored in Amazon S3. What should the engineer first investigate to troubleshoot this performance issue?

Select 1 option(s):

Check the Amazon S3 bucket policies to ensure there are no access restrictions slowing down data retrieval.

Increase the number of Data Processing Units (DPUs) allocated to the AWS Glue job.

Examine the Amazon CloudWatch Logs for errors or warnings related to the AWS Glue job.

Review the VPC configurations to ensure network connectivity is not impacting the job‘s performance.

Q36. You are working on a project where you need to create and manage AWS resources for a data pipeline. You want to define the infrastructure as code and have the ability to use a visual designer to model and provision the resources. Which AWS service allows you to create, view, and modify AWS resource templates using a visual interface while still leveraging infrastructure as code?

Select 1 option(s):

AWS Data Pipeline

AWS Glue

AWS Cloud Development Kit (AWS CDK)

AWS CloudFormation Designer

Q37. A data engineering team is operating a large-scale ETL process using AWS Glue. They need to monitor the performance and health of this process closely. Which AWS service should they use to set up detailed monitoring and receive alerts if specific metrics go beyond predefined thresholds?

Select 1 option(s):

Implement AWS CloudTrail to monitor the ETL process.

Use Amazon CloudWatch to monitor metrics and set up alarms.

Apply AWS Config for real-time monitoring of the AWS Glue environment.

Utilize Amazon QuickSight for continuous performance monitoring.

AWS Data Engineer Associate - Test 16

Q1. You are designing a recommendation engine for an e-commerce platform that needs to analyze the relationships between products and user interactions to make personalized product recommendations. Which data structure is most suitable for representing and analyzing these complex relationships?

Q2. An organization needs to manage and rotate credentials used by its EC2 instances to access a database securely. They require a solution that integrates with AWS Identity and Access Management (IAM) for access control. Which AWS service should they use for this purpose?

Q3. You are working with a large dataset, and a complex SQL query is taking a significant amount of time to complete. After reviewing the query execution plan, you notice that there are multiple nested subqueries. What is a recommended strategy to optimize the query?

Q4. When preparing a dataset for machine learning in Amazon SageMaker Data Wrangler, a data scientist discovers that some categorical data is inconsistently formatted. What should the scientist do within Data Wrangler to standardize these categorical values?

Q5. A data engineering team is concerned about securing sensitive data stored in their Amazon S3 buckets. They want to automatically identify and classify sensitive information like personally identifiable information (PII). Which AWS service should they use for this purpose?

Q6. A data engineering team wants to audit and monitor all data-related activities within their AWS environment, including changes to data resources and access to sensitive data. Which AWS service should they primarily use to achieve comprehensive logging of these activities?

Q7. A development team is building a serverless application on AWS that interacts with an Amazon RDS database. They need a secure and efficient way to store and retrieve the database credentials. Which AWS service is most suitable for storing these database credentials?

Q8. A company uses Amazon Redshift for its data warehousing needs. They are concerned about protecting their data, both at rest and in transit. Which of the following options should they choose to ensure their data in Amazon Redshift is encrypted?

Q11. A financial institution needs to share transactional data with a third-party analytics service but must adhere to GDPR regulations by not exposing any customers personal data. What method should they use to prepare this data for sharing?

Q12. A company stores its sales data in JSON format in Amazon S3. The data engineering team needs to use Amazon Athena to run ad-hoc queries on this data. They want to create a structured view of the data for easier querying. What is the first step the team should take in Athena?

Q13. A data engineering team is deploying a new application on AWS and wants to ensure that all operational logs are centrally collected and easily accessible for analysis. Which AWS service should they primarily use for deploying and managing these application logs?

Q16. A team is using AWS Glue for ETL processes. They have noticed that some jobs are taking longer than expected. What action can the team take to improve the performance of their AWS Glue jobs?

Q17. A financial institution needs to delete customer transaction records that are older than seven years to comply with data retention laws. What is the most appropriate AWS service or feature to implement this data deletion strategy?

Q18. A healthcare organization is migrating its patient records to AWS and needs to comply with HIPAA regulations regarding patient data. They want to analyze the data without exposing sensitive patient information. What approach should they take to ensure compliance while enabling data analysis?

Q20. An organization is using Athena notebooks with Apache Spark to analyze real-time streaming data from IoT devices. The data is ingested into Amazon Kinesis and needs to be explored in the notebook. Which approach should the data engineer take to analyze this streaming data in the notebook?

Q21. A data engineer is designing a data storage solution using Amazon DynamoDB. They need to automatically delete items from a DynamoDB table when they reach a certain age. Which feature of DynamoDB should they configure to achieve this?

Q22. A company wants to ensure that their marketing team can query only marketing-related data using Amazon Athena, while the data is governed and secured by AWS Lake Formation. What is the best approach to achieve this?

Q23. A healthcare organization needs to delete patient medical records that have reached their mandated retention period of ten years. These records are stored in Amazon RDS. What is the recommended approach to delete this data securely and efficiently?

Q26. A media streaming company relies on Amazon S3 to store its video content. They want to protect their data against accidental deletions and require versioning to be enabled. Which option should the company choose to meet these requirements?

Q27. A data engineering team is evaluating encryption options for their new Amazon Redshift cluster that will store sensitive customer data. They need to comply with regulatory requirements for encryption of data at rest. What should they implement to meet this requirement?

Q29. An organization is using multiple AWS services for their application, including Amazon EC2, RDS, and Lambda. They need a centralized solution to manage and rotate credentials used by these services. What is the most suitable AWS service for this requirement?

Q31. An organization utilizes Amazon Redshift for storing sensitive financial data. They need to ensure that only specific database administrators can modify the data, while other team members can only run queries. What is the appropriate authorization mechanism to implement this in Amazon Redshift?

Q32. An organization is using AWS Lambda functions as part of their data processing pipeline. They want to be immediately alerted if any Lambda function runs into errors or exceeds execution time thresholds. Which AWS services should they use to achieve this?

Q35. A data engineer notices that an AWS Glue ETL job is taking significantly longer to complete than expected. The job processes a large dataset stored in Amazon S3. What should the engineer first investigate to troubleshoot this performance issue?

Q37. A data engineering team is operating a large-scale ETL process using AWS Glue. They need to monitor the performance and health of this process closely. Which AWS service should they use to set up detailed monitoring and receive alerts if specific metrics go beyond predefined thresholds?

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AWS Data Engineer Associate - Test 16

Q1. You are designing a recommendation engine for an e-commerce platform that needs to analyze the relationships between products and user interactions to make personalized product recommendations. Which data structure is most suitable for representing and analyzing these complex relationships?

Q2. An organization needs to manage and rotate credentials used by its EC2 instances to access a database securely. They require a solution that integrates with AWS Identity and Access Management (IAM) for access control. Which AWS service should they use for this purpose?

Q3. You are working with a large dataset, and a complex SQL query is taking a significant amount of time to complete. After reviewing the query execution plan, you notice that there are multiple nested subqueries. What is a recommended strategy to optimize the query?

Q4. When preparing a dataset for machine learning in Amazon SageMaker Data Wrangler, a data scientist discovers that some categorical data is inconsistently formatted. What should the scientist do within Data Wrangler to standardize these categorical values?

Q5. A data engineering team is concerned about securing sensitive data stored in their Amazon S3 buckets. They want to automatically identify and classify sensitive information like personally identifiable information (PII). Which AWS service should they use for this purpose?

Q6. A data engineering team wants to audit and monitor all data-related activities within their AWS environment, including changes to data resources and access to sensitive data. Which AWS service should they primarily use to achieve comprehensive logging of these activities?

Q7. A development team is building a serverless application on AWS that interacts with an Amazon RDS database. They need a secure and efficient way to store and retrieve the database credentials. Which AWS service is most suitable for storing these database credentials?

Q8. A company uses Amazon Redshift for its data warehousing needs. They are concerned about protecting their data, both at rest and in transit. Which of the following options should they choose to ensure their data in Amazon Redshift is encrypted?

Q11. A financial institution needs to share transactional data with a third-party analytics service but must adhere to GDPR regulations by not exposing any customers personal data. What method should they use to prepare this data for sharing?

Q12. A company stores its sales data in JSON format in Amazon S3. The data engineering team needs to use Amazon Athena to run ad-hoc queries on this data. They want to create a structured view of the data for easier querying. What is the first step the team should take in Athena?

Q13. A data engineering team is deploying a new application on AWS and wants to ensure that all operational logs are centrally collected and easily accessible for analysis. Which AWS service should they primarily use for deploying and managing these application logs?

Q16. A team is using AWS Glue for ETL processes. They have noticed that some jobs are taking longer than expected. What action can the team take to improve the performance of their AWS Glue jobs?

Q17. A financial institution needs to delete customer transaction records that are older than seven years to comply with data retention laws. What is the most appropriate AWS service or feature to implement this data deletion strategy?

Q18. A healthcare organization is migrating its patient records to AWS and needs to comply with HIPAA regulations regarding patient data. They want to analyze the data without exposing sensitive patient information. What approach should they take to ensure compliance while enabling data analysis?

Q20. An organization is using Athena notebooks with Apache Spark to analyze real-time streaming data from IoT devices. The data is ingested into Amazon Kinesis and needs to be explored in the notebook. Which approach should the data engineer take to analyze this streaming data in the notebook?

Q21. A data engineer is designing a data storage solution using Amazon DynamoDB. They need to automatically delete items from a DynamoDB table when they reach a certain age. Which feature of DynamoDB should they configure to achieve this?

Q22. A company wants to ensure that their marketing team can query only marketing-related data using Amazon Athena, while the data is governed and secured by AWS Lake Formation. What is the best approach to achieve this?

Q23. A healthcare organization needs to delete patient medical records that have reached their mandated retention period of ten years. These records are stored in Amazon RDS. What is the recommended approach to delete this data securely and efficiently?

Q26. A media streaming company relies on Amazon S3 to store its video content. They want to protect their data against accidental deletions and require versioning to be enabled. Which option should the company choose to meet these requirements?

Q27. A data engineering team is evaluating encryption options for their new Amazon Redshift cluster that will store sensitive customer data. They need to comply with regulatory requirements for encryption of data at rest. What should they implement to meet this requirement?

Q29. An organization is using multiple AWS services for their application, including Amazon EC2, RDS, and Lambda. They need a centralized solution to manage and rotate credentials used by these services. What is the most suitable AWS service for this requirement?

Q32. An organization is using AWS Lambda functions as part of their data processing pipeline. They want to be immediately alerted if any Lambda function runs into errors or exceeds execution time thresholds. Which AWS services should they use to achieve this?

Q35. A data engineer notices that an AWS Glue ETL job is taking significantly longer to complete than expected. The job processes a large dataset stored in Amazon S3. What should the engineer first investigate to troubleshoot this performance issue?

Q37. A data engineering team is operating a large-scale ETL process using AWS Glue. They need to monitor the performance and health of this process closely. Which AWS service should they use to set up detailed monitoring and receive alerts if specific metrics go beyond predefined thresholds?

Q11. A financial institution needs to share transactional data with a third-party analytics service but must adhere to GDPR regulations by not exposing any customers personal data. What method should they use to prepare this data for sharing?