MLS-C01 Data Engineering Practice Question
A data engineer is building a data pipeline that uses AWS Lambda to process records from an SQS queue and write results to an S3 bucket. The Lambda function processes each record individually and writes a separate file to S3. The team notices high latency and wants to reduce the number of S3 PUT requests to improve performance and reduce cost. Which approach should the data engineer take?
⚠ Common exam trap
Many candidates confuse 'multipart upload' (Option A) with batching, but multipart upload is for large files, not for reducing the count of small PUT requests, and they may overlook that S3 Batch Operations (Option C) is a post-ingestion tool, not a streaming aggregation mechanism.
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
✓
Aggregate multiple records into a single file in a DynamoDB table, then periodically write the aggregated data to S3.
It reduces the number of S3 PUT requests by aggregating multiple records into a single file in DynamoDB and then periodically writing the aggregated data to S3. This approach directly addresses the high latency and cost issue caused by writing a separate S3 object per record, as S3 PUT requests are billed per operation and have overhead. By batching records before writing, the pipeline reduces the total number of PUT requests, improving throughput and lowering costs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use S3 multipart upload for each record to improve throughput.
Why it's wrong here
Multipart upload is for large objects; for small records it adds overhead.
- ✗
Increase the Lambda function's memory allocation to improve processing speed.
Why it's wrong here
More memory may speed up processing but does not reduce the number of S3 PUT requests.
- ✗
Use S3 Batch Operations to process the records in batches.
Why it's wrong here
S3 Batch Operations are for batch processing of existing objects, not for incoming streaming data.
- ✓
Aggregate multiple records into a single file in a DynamoDB table, then periodically write the aggregated data to S3.
Why this is correct
Aggregation reduces the number of S3 PUT requests by writing larger files less frequently.
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
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.