AWS Certified Data Engineer Associate DEA-C01 Practice Question
Your company stores raw application logs as gzip files in an S3 prefix. A pandas script is already available that reads the files, calculates record counts, and writes a small JSON summary back to another prefix. The script should run automatically when an S3 ObjectCreated event is received, must finish within minutes, and should incur the lowest Glue cost. Which AWS Glue option should you configure?
Launch an AWS Glue interactive session that continuously polls S3 for new objects.
Build an AWS Glue Spark ETL job in PySpark and enable job bookmarking for incremental processing.
Create an AWS Glue Python shell job and trigger it with an EventBridge rule.
Configure an AWS Glue crawler to run whenever an S3 ObjectCreated event is emitted.
An AWS Glue Python shell job is designed for lightweight ETL tasks where a full Spark cluster is unnecessary. It can execute a standalone Python script that relies on libraries such as pandas, starts quickly, and is billed per second for only one-quarter DPU, making it the least-expensive Glue runtime. EventBridge rules can invoke the job when an S3 ObjectCreated event occurs. A Spark ETL job would launch a distributed cluster, adding cost and startup latency. Crawlers catalogue data; they do not execute custom scripts. Interactive sessions are intended for ad-hoc development, remain active while you work, and are not cost-efficient for event-driven production workloads.
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What is an AWS Glue Python shell job?
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How does EventBridge work with S3 events?
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Why is an AWS Glue Spark ETL job less suitable in this scenario?
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What is an AWS Glue Python shell job?
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How does EventBridge integrate with AWS Glue?
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Why is using Spark ETL jobs less suitable for this task?
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AWS Certified Data Engineer Associate DEA-C01
Data Operations and Support
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