DEA-C01 Data Operations and Support Practice Question

A company ingests IoT sensor data into an S3 bucket. Daily, a Lambda function reads new objects, processes them, and writes results to a DynamoDB table. Recently, the Lambda function started timing out after 15 minutes. The data volume has increased, and the function processes records one by one. Which solution would improve performance without significant cost increase?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Use S3 Batch Operations to invoke the Lambda function in parallel for each object.

S3 Batch Operations invokes the Lambda function for each object in parallel, efficiently handling increased volume without significant cost increase. Option A is incorrect because AWS Glue ETL jobs have startup overhead and may cost more. Option B is incorrect because increasing the timeout does not address the root cause of sequential processing; the function would still process records one by one and may still timeout. Option D is incorrect because increasing DynamoDB write capacity does not speed up the Lambda processing; the bottleneck is the sequential processing within the function.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Replace Lambda with an AWS Glue ETL job.
Why it's wrong here
Glue may be slower to start and more expensive for simple transformations.
✗
Increase the Lambda function timeout to 30 minutes.
Why it's wrong here
Increasing timeout only delays failure; the function still processes sequentially.
✓
Use S3 Batch Operations to invoke the Lambda function in parallel for each object.
Why this is correct
S3 Batch Operations processes objects concurrently, drastically reducing processing time.
✗
Increase the DynamoDB write capacity units.
Why it's wrong here
The bottleneck is the Lambda processing, not DynamoDB writes.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

Go deeper

DEA-C01 Data Operations and Support Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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