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AWS Certified Data Engineer Associate DEA-C01 Practice Question

A company stores CSV files of about 50 MB each in an Amazon S3 landing bucket every 5 minutes. A data engineer must automatically convert each file to Parquet, add an ingestion_timestamp column, and write the result to a separate S3 bucket organized by date and hour. The solution must remain fully serverless, minimize cost, and require little operational management. Which approach meets these requirements?

  • Configure an S3 event notification that invokes an AWS Lambda function using the AWS SDK for pandas to read the CSV object, add the timestamp column, and write a Parquet file to the destination bucket's date/hour prefix.

  • Use a Lambda function to start an on-demand Amazon EMR cluster that runs a conversion script and terminates the cluster after processing each batch of files.

  • Trigger an AWS Step Functions state machine from the S3 event that starts an AWS Glue Spark job to perform the conversion and load the result to the destination bucket.

  • Send the files to an Amazon Kinesis Data Firehose delivery stream that uses a Lambda transformation to convert records to Parquet before writing to the target bucket.

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