AWS Certified Data Engineer Associate DEA-C01 Practice Question
A data engineering team uses an AWS Lambda function written in Python to convert incoming CSV files to Apache Parquet. The function is invoked by Amazon S3 event notifications and writes the output to another bucket. During seasonal traffic, up to 3,000 objects arrive within one minute, leading to frequent cold starts, longer execution duration, and CPU starvation. The team must improve runtime performance and sustain peak throughput without modifying the application code. Which action meets these requirements MOST effectively?
Increase the function timeout value and enable the maximum number of asynchronous invocation retry attempts.
Replace the S3 trigger with an Amazon SQS queue and set the Lambda function's reserved concurrency to 1 so that files are processed sequentially.
Increase the function memory size and configure provisioned concurrency to match the expected peak number of parallel invocations.
Migrate the code to an AWS Glue job that runs on a schedule every minute to batch process new files.
Increasing a Lambda function's memory allocation also proportionally increases the amount of CPU and network bandwidth that the function receives. Pairing a higher memory setting with provisioned concurrency keeps a pool of pre-initialized execution environments ready to respond, virtually eliminating cold starts even during traffic spikes. This combination reduces average run time and sustains thousands of concurrent invokes. Simply extending the timeout or enabling retries does not address cold starts or CPU limits. Processing files sequentially by limiting concurrency would throttle throughput and increase overall latency. Migrating to AWS Glue introduces additional operational overhead and does not specifically target the cold-start and CPU-bound nature of the existing Lambda workload.
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AWS Certified Data Engineer Associate DEA-C01
Data Ingestion and Transformation
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