AWS Certified Data Engineer - Associate Practice Exam: Test Your Knowledge 2025

A data engineering team needs to ingest streaming data from IoT devices into AWS for real-time analytics. The solution must automatically scale to handle variable throughput and require minimal operational overhead. Which service should the team use?

A company stores its data lake in Amazon S3 and uses AWS Glue to catalog the data. Users report that queries against the data catalog are returning outdated schema information after recent data updates. What is the MOST efficient solution to ensure the catalog remains current?

A data engineer needs to monitor an AWS Glue ETL job for failures and send notifications when errors occur. What is the recommended approach to implement this monitoring solution?

A financial services company needs to ensure that sensitive customer data stored in Amazon S3 is encrypted at rest and that they maintain full control over the encryption keys, including the ability to rotate them. Which encryption option should they use?

A data engineering team is designing a data pipeline to process log files stored in S3. The processing involves filtering, transforming, and aggregating the data before loading it into Amazon Redshift. Which AWS service provides a serverless, fully managed ETL solution for this use case?

A company is building a data lake on AWS and needs to transform JSON data stored in S3 into Parquet format to optimize query performance and reduce storage costs. The transformation should be automated and cost-effective. Which combination of services should be used?

A data engineer is designing a solution to ingest data from multiple on-premises databases into AWS. The solution must support CDC (Change Data Capture) to replicate only changed data and minimize the impact on source systems. Which AWS service should be used?

A company uses Amazon Redshift for analytics and needs to improve query performance for frequently accessed dimension tables that are relatively small. What Redshift feature should be implemented?

A data pipeline processes files uploaded to S3 using AWS Lambda. Recently, large files have been causing Lambda timeouts. The processing involves data validation, transformation, and loading into DynamoDB. What is the BEST solution to handle large files without refactoring the entire pipeline?

A company stores petabytes of log data in Amazon S3 and uses Amazon Athena for ad-hoc queries. Query costs are becoming expensive due to the amount of data scanned. What combination of optimizations will MOST effectively reduce costs? (Choose the BEST answer)

A data engineering team needs to orchestrate a complex ETL workflow with multiple dependencies, conditional logic, and error handling. The workflow includes AWS Glue jobs, Lambda functions, and Amazon EMR steps. Which service provides the MOST comprehensive solution for this orchestration?

A company needs to implement fine-grained access control for their data lake in Amazon S3, allowing different teams to access only specific databases and tables cataloged in AWS Glue. What AWS service should be used to implement this governance layer?

A data engineer is optimizing an Amazon Redshift cluster that experiences variable query loads throughout the day. The cluster is oversized for off-peak hours but struggles during peak times. What is the MOST cost-effective solution to handle this variable workload?

A company is using AWS Glue to process sensitive healthcare data. They need to ensure that personally identifiable information (PII) is automatically detected and masked before the data is stored in S3. Which AWS Glue feature should be implemented?

A data pipeline ingests streaming data from Amazon Kinesis Data Streams and processes it using a Lambda function before storing results in DynamoDB. The Lambda function occasionally fails due to throttling from DynamoDB. What is the BEST approach to handle this issue?

A company needs to migrate 500 TB of historical data from an on-premises Hadoop cluster to Amazon S3 as quickly as possible. The company has limited network bandwidth (100 Mbps). Which approach will complete the migration FASTEST?

A data engineer is building a real-time analytics pipeline that ingests clickstream data, performs aggregations over tumbling windows of 5 minutes, and stores results in Amazon S3. The solution must be fully managed and serverless. Which combination of services should be used?

A company runs complex analytical queries on Amazon Redshift that join large fact tables with multiple dimension tables. Despite proper distribution keys, queries are still slow due to data skew on the fact table. What is the MOST effective strategy to address this performance issue?

A data engineering team is implementing a data lake solution where data flows through Bronze (raw), Silver (cleansed), and Gold (aggregated) layers. They need to implement a solution that tracks data lineage, maintains metadata, and enables time travel queries. The solution should integrate seamlessly with existing Spark-based ETL jobs on AWS Glue. What technology should be used?

A financial institution needs to implement a data pipeline that processes transaction data with the following requirements: exactly-once processing semantics, ability to reprocess data from any point in time, ordered processing per customer, and high throughput. The processed data must be stored in Amazon S3. Which architecture BEST meets these requirements?

A company has a multi-account AWS environment and needs to centralize access logs from S3 buckets across all accounts for compliance auditing. The solution must ensure logs cannot be modified or deleted by any account, including the root user. What is the MOST secure approach?

A data pipeline uses AWS Glue jobs to process daily data files from S3. The team notices that the Glue jobs are processing the same files multiple times, causing duplicate records in the target database. What feature should be enabled to prevent reprocessing of already processed data?