- A
Use S3 multipart upload for each record to improve throughput.
Why wrong: Multipart upload is for large objects; for small records it adds overhead.
- B
Increase the Lambda function's memory allocation to improve processing speed.
Why wrong: More memory may speed up processing but does not reduce the number of S3 PUT requests.
- C
Use S3 Batch Operations to process the records in batches.
Why wrong: S3 Batch Operations are for batch processing of existing objects, not for incoming streaming data.
- D
Aggregate multiple records into a single file in a DynamoDB table, then periodically write the aggregated data to S3.
Aggregation reduces the number of S3 PUT requests by writing larger files less frequently.
Question 1,185 of 1,755
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Option D is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Under the hood, S3 PUT requests have a per-request cost and latency overhead, so writing thousands of small files individually can quickly become expensive and slow. Aggregating records in DynamoDB leverages its low-latency writes and then uses a scheduled job (e.g., EventBridge + Lambda) to flush the aggregated data as a single compressed file (e.g., Parquet or JSON Lines) to S3, reducing PUT requests by orders of magnitude. In real-world scenarios, this pattern is common in streaming pipelines where the trade-off between near-real-time latency and cost is managed by tuning the aggregation window (e.g., every 5 minutes or every 1000 records).
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 |
What to study next
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Aggregate multiple records into a single file in a DynamoDB table, then periodically write the aggregated data to S3. — Option D is correct because 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.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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