AWS Certified Data Engineer Associate DEA-C01 Practice Question
An e-commerce company stores order data as daily Parquet files in an Amazon S3 prefix. A data engineer builds an AWS Glue Spark job that runs hourly to load new data into Amazon Redshift. The job must skip files that were already processed and should replay those files only if the job is rerun after a failure. Which AWS Glue feature should the engineer enable to meet these requirements?
Configure continuous logging to Amazon CloudWatch Logs.
Turn on speculative execution in the Spark configuration.
Set the job type to streaming and process the data with Apache Hudi.
AWS Glue job bookmarks persist state information that tracks which data files a job has already processed. When the job runs again, Glue automatically filters out the previously processed files, so the job ingests only new data. If the job fails and is rerun, the bookmark can be reset or used in "job-bookmark" mode to reprocess the last committed batch, providing the required replayability. Continuous logging, speculative execution, or changing the job to a streaming Hudi job do not provide automatic, stateful tracking of previously processed batch files.
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AWS Certified Data Engineer Associate DEA-C01
Data Ingestion and Transformation
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