AWS Certified Data Engineer - Associate Intermediate Practice Exam: Medium Difficulty 2025

A data engineering team is building a streaming data pipeline that ingests clickstream data from a web application into Amazon Kinesis Data Streams. The data must be transformed to enrich it with user profile information from Amazon RDS before being stored in Amazon S3 for analytics. The solution should minimize operational overhead and automatically scale based on incoming data volume. Which approach should the team implement?

A financial services company stores transaction data in Amazon S3 in Parquet format, partitioned by year, month, and day. The data analytics team frequently queries data from the last 30 days using Amazon Athena, but query costs are high due to the large volume of data scanned. The team wants to reduce costs while maintaining query performance. What combination of actions should be taken? (Choose the BEST answer)

A data engineering team is designing an ETL pipeline using AWS Glue to process daily sales data from multiple sources. The pipeline must handle schema evolution, validate data quality, and send notifications when data quality checks fail. The team wants to implement this with minimal custom code. Which AWS Glue features should be combined to meet these requirements?

A healthcare company needs to implement a data lake on AWS where sensitive patient data must be encrypted at rest and in transit. Different departments require different levels of access to the data based on data classification. The solution must provide centralized access control and audit logging. Which combination of AWS services best meets these security requirements?

A retail company runs nightly AWS Glue ETL jobs that process large datasets from S3, perform complex transformations, and load results into Amazon Redshift. Recently, jobs have been failing intermittently with out-of-memory errors during the join operations on large fact and dimension tables. The company wants to optimize the jobs without significantly increasing costs. What approach should be taken?

A media company ingests real-time video metadata from IoT devices into Amazon Kinesis Data Streams. The metadata needs to be processed and aggregated over 5-minute windows before being stored in Amazon DynamoDB for a dashboard application. The processing logic includes filtering, transformation, and time-based aggregation. Which solution provides the most appropriate architecture?

A data engineering team manages an Amazon Redshift cluster that serves multiple business intelligence tools. The cluster experiences performance degradation during business hours when concurrent queries increase. Analysis shows that long-running analytical queries are blocking short-running dashboard queries. The team needs to ensure dashboard queries maintain consistent performance. What is the most effective solution?

A company's data lake contains personally identifiable information (PII) that must be discovered, classified, and masked before data scientists can access it for model training. The company needs an automated solution that integrates with their existing AWS Glue Data Catalog and provides ongoing PII detection as new data arrives. Which approach should be implemented?

A data pipeline processes JSON log files from S3 using AWS Glue ETL jobs and loads them into Amazon Redshift for analysis. The team notices that the daily job duration has increased from 30 minutes to 3 hours as data volume has grown, and the Redshift COPY operations are taking longer. What combination of optimizations would most effectively improve performance?

A financial institution is designing a data pipeline where transaction records from Amazon Kinesis Data Streams must be processed and stored with strong consistency guarantees and the ability to query data using both key-value lookups and complex SQL queries. The solution must support ACID transactions and handle up to 10,000 transactions per second. Which data store combination best meets these requirements?