Crucial Exams

AWS Certified Data Engineer Associate DEA-C01 Practice Question

An e-commerce company stores daily transaction CSV files in Amazon S3. The downstream ML pipeline fails whenever numeric columns contain null or non-numeric values. You need an automated, low-code solution that validates and cleans each new file, stores the corrected data in a curated S3 prefix, and provides a summary of invalid records. Which approach requires the least operational effort?

  • Invoke an Amazon Athena CTAS query from an AWS Lambda function each day to select only valid rows into a new table stored in a different S3 prefix and publish results to Amazon SNS.

  • Build a custom Docker image that uses pandas to clean the files and run it daily with AWS Batch, writing logs of invalid rows to Amazon CloudWatch Logs.

  • Spin up an Amazon EMR cluster running Apache Spark, develop a PySpark script to validate and cleanse the dataset, and schedule the job with AWS Step Functions.

  • Create an AWS Glue DataBrew project that applies data quality rules, schedule a recipe job to output cleaned data to a curated S3 prefix, and rely on the job run metrics for the invalid-row summary.

AWS Certified Data Engineer Associate DEA-C01
Data Operations and Support
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