Amazon DATA-ENGINEER-ASSOCIATE Online Practice Questions and Exam Preparation

Amazon DATA-ENGINEER-ASSOCIATE Online Questions & Answers

• Question 341:

  A company wants to use Apache Spark jobs that run on an Amazon EMR cluster to process streaming data. The Spark jobs will transform and store the data in an Amazon S3 bucket. The company will use Amazon Athena to perform analysis. The company needs to optimize the data format for analytical queries.

  Which solutions will meet these requirements with the SHORTEST query times? (Choose two.)

  A. Use Avro format. Use AWS Glue Data Catalog to track schema changes.
  B. Use ORC format. Use AWS Glue Data Catalog to track schema changes.
  C. Use Apache Parquet format. Use an external Amazon DynamoDB table to track schema changes.
  D. Use Apache Parquet format. Use AWS Glue Data Catalog to track schema changes.
  E. Use ORC format. Store schema definitions in separate files in Amazon S3.
  B. Use ORC format. Use AWS Glue Data Catalog to track schema changes.
  D. Use Apache Parquet format. Use AWS Glue Data Catalog to track schema changes.

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  Explanation

  ORC is a columnar format optimized for Athena and provides high compression and predicate pushdown for faster queries; Glue Data Catalog manages schema evolution.

  Parquet is a columnar format optimized for Athena with efficient compression and predicate pushdown;

  Glue Data Catalog tracks schema changes.

• Question 342:

  A data engineer needs to validate the quality of files that are uploaded to an Amazon S3 bucket every day.

  The files are in CSV and JSON formats and schema variations exist. The data engineer needs a repeatable process to monitor data quality metrics such as null values, format inconsistencies, and outliers. The process must provide reusable rule-based profiling and scale across multiple datasets with minimal manual effort or custom code.

  Which solution will meet these requirements?

  A. Use AWS Glue Studio to create a daily extract, transform, and load (ETL) pipeline that merges datasets and applies validation logic by using PySpark transformations.
  B. Use AWS Glue extract, transform, and load (ETL) jobs with dynamic frames and the ResolveChoice class to align schemas and detect structural differences between files.
  C. Use AWS Glue DataBrew to configure profiling jobs and reusable recipe actions. Schedule the profiling jobs and reusable recipe actions to run against each dataset in Amazon S3.
  D. Use AWS Glue DataBrew to generate profiling dashboards for each dataset. Monitor quality metrics through manual review.
  C. Use AWS Glue DataBrew to configure profiling jobs and reusable recipe actions. Schedule the profiling jobs and reusable recipe actions to run against each dataset in Amazon S3.

• Question 343:

  A Lambda function transforms files that arrive in Amazon S3. The function sometimes creates too many simultaneous connections to a downstream database. The data engineer must cap the function's concurrent executions without changing the upstream S3 event source.

  Which configuration should the data engineer use?

  A. Configure reserved concurrency on the Lambda function.
  B. Enable S3 Versioning on the source bucket.
  C. Increase the Lambda memory setting to the maximum value.
  D. Create a Glue crawler for the source bucket.
  A. Configure reserved concurrency on the Lambda function.

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  Explanation

  Reserved concurrency sets a maximum number of concurrent executions for a Lambda function and can protect downstream systems. S3 Versioning affects object history, not Lambda concurrency. More memory can improve CPU allocation but does not cap concurrent invocations. A Glue crawler updates metadata and does not throttle Lambda.

• Question 344:

  A company needs to implement a workflow to process transactions. Each transaction goes through multiple levels of validation. Each validation level depends on the preceding validation level. The workflow must either process or reject each transaction within 24-hours. The workflow must run for less than 24 hours total.

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

  A. Create a standard workflow in AWS Step Functions. Implement a Wait for Callback pattern to wait for the validation steps to finish.
  B. Create an express workflow in AWS Step Functions. Implement a Wait for Callback pattern to wait for the validation steps to finish.
  C. Use AWS Lambda functions to implement the workflow. Use Amazon EventBridge to invoke the validation steps.
  D. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to implement the workflow.
  A. Create a standard workflow in AWS Step Functions. Implement a Wait for Callback pattern to wait for the validation steps to finish.

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  Explanation

  Standard Step Functions support long-running, sequential workflows and the Wait for Callback pattern without charging for idle wait time, minimizing cost while ensuring completion within 24 hours. Express would bill for long waits, Lambda +EventBridge adds complexity and limits, and MWAA has higher operational overhead and cost.

• Question 345:

  Which service should a company use to minimize effort when integrating third-party datasets into its analytics platform?

  A. AWS Data Exchange via API calls
  B. AWS DataSync via API calls
  C. Amazon Kinesis Data Streams with AWS CodeCommit
  D. Amazon Kinesis Data Streams with Amazon ECR
  A. AWS Data Exchange via API calls

• Question 346:

  A company has as JSON file that contains personally identifiable information (PII) data and non-PII data.

  The company needs to make the data available for querying and analysis.

  The non-PII data must be available to everyone in the company. The PII data must be available only to a limited group of employees.

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

  A. Store the JSON file in an Amazon S3 bucket. Configure AWS Glue to split the file into one file that contains the PII data and one file that contains the non-PII data. Store the output files in separate S3 buckets. Grant the required acce to the buckets based on the type of user.
  B. Store the JSON file in an Amazon S3 bucket. Use Amazon Macie to identify PII data and to grant acce based on the type of user.
  C. Store the JSON file in an Amazon S3 bucket. Catalog the file schema in AWS Lake Formation. Use Lake Formation permiions to provide acce to the required data based on the type of user.
  D. Create two Amazon RDS PostgreSQL databases. Load the PII data and the non-PII data into the separate databases. Grant acce to the databases based on the type of user.
  C. Store the JSON file in an Amazon S3 bucket. Catalog the file schema in AWS Lake Formation. Use Lake Formation permiions to provide acce to the required data based on the type of user.

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  Explanation

  AWS Lake Formation lets you register your JSON file as a table in the data catalog and apply fine-grained permiions (down to columns or rows) without moving or transforming the underlying data. You can grant all employees acce to the non-PII columns while restricting the PII columns to a small group, achieving your security requirements with minimal operational overhead.

• Question 347:

  How can a data engineer avoid manually updating multiple Lambda functions when modifying shared Python scripts?

  A. Store script pointers in the execution context object in S3
  B. Package scripts into Lambda layers and apply to the functions
  C. Store script pointers in S3 bucket environment variables
  D. Assign the same alias to all Lambda functions
  B. Package scripts into Lambda layers and apply to the functions

• Question 348:

  A research company stores data in an Amazon Redshift cluster. The company needs to share data between departments and maintain regulatory compliance. The company needs a solution that gives researchers access to only the records from their own departments and does not create multiple dataset copies. The solution must also ensure that personally identifiable information (PII) is protected from unauthorized access.

  Which solution will meet these requirements?

  A. Create a datashare in Amazon Redshift for each department. Use cross-Region data sharing to distribute copies of the entire dataset to each department's Amazon Redshift cluster.
  B. Implement row-level security policies with basic SQL filters based on department. Attach the security policies to the data tables. Grant EXPLAIN RLS permission to authorized researchers.
  C. Create separate schemas for each department with appropriate views that filter data. Grant each department access to only their respective schema.
  D. Use row-level security policies with multi-condition SQL predicates. Attach the security policies to the data tables. Grant each department's role access to the appropriate policies.
  D. Use row-level security policies with multi-condition SQL predicates. Attach the security policies to the data tables. Grant each department's role access to the appropriate policies.

• Question 349:

  An application writes session records to Amazon DynamoDB. Each session item must be deleted automatically 30 days after the session expires. The application can calculate the expiration time when it writes the item.

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

  A. Enable DynamoDB TTL on the table and write an epoch-time expiration attribute to each session item.
  B. Create an S3 Lifecycle rule for a prefix that matches the DynamoDB table name.
  C. Schedule an AWS Glue job to scan the entire DynamoDB table every hour and delete old items.
  D. Create a DynamoDB global secondary index on the session expiration date and manually delete expired items each day.
  A. Enable DynamoDB TTL on the table and write an epoch-time expiration attribute to each session item.

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  Explanation

  DynamoDB TTL uses an item attribute that contains an expiration timestamp in epoch seconds and automatically removes expired items. S3 Lifecycle rules do not apply to DynamoDB tables. A Glue scan or manual deletion process adds cost and operational work that native TTL avoids.

• Question 350:

  A data lake receives a small manifest file in an S3 prefix whenever a vendor upload is complete. A transformation should start as soon as the manifest file arrives. The team wants an event-driven solution without polling.

  Which solution should the data engineer implement?

  A. Configure S3 Event Notifications with a suffix filter for the manifest file and invoke the transformation target.
  B. Configure a Glue crawler to run every 12 hours and infer the manifest file schema.
  C. Configure S3 Lifecycle to transition manifest files to S3 Glacier Instant Retrieval.
  D. Configure an Athena scheduled query to scan the S3 prefix every minute.
  A. Configure S3 Event Notifications with a suffix filter for the manifest file and invoke the transformation target.

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  Explanation

  S3 Event Notifications can trigger processing when objects are created and can filter by prefix or suffix so only manifest files start the transformation. A crawler schedule introduces delay and is for catalog discovery. S3 Lifecycle changes storage class and does not start processing. An Athena polling query adds avoidable cost and latency.