DEA-C01 Data Operations and Support Practice Question
A company runs a data pipeline using AWS Lambda to process records from an Amazon Kinesis Data Stream. Recently, the Lambda function has been experiencing high invocation errors and the stream is throttling. The function performs simple transformations and writes to Amazon S3. What is the most effective way to reduce throttling and errors?
⚠ Common exam trap
The trap here is that candidates mistakenly believe throttling is caused by Lambda concurrency limits or cold starts, when in fact the root cause is the Kinesis stream's read throughput limit per shard, which is reduced by increasing the batch size in the event source mapping.
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
✓
Increase the batch size in the Lambda event source mapping.
Increasing the batch size in the Lambda event source mapping allows each invocation to process more records from the Kinesis stream, reducing the number of total invocations. This lowers the rate at which Lambda polls the stream, which decreases the likelihood of hitting the Kinesis read throughput limits (5 transactions per second per shard) and reduces throttling errors. The simple transformations and S3 writes are likely I/O-bound, so larger batches improve throughput without increasing invocation concurrency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the Lambda function timeout.
Why it's wrong here
Timeout does not affect throttling or invocation errors.
- ✗
Enable provisioned concurrency on the Lambda function.
Why it's wrong here
Provisioned concurrency keeps instances warm but does not reduce throttling from the stream.
- ✗
Increase the number of shards in the Kinesis stream.
Why it's wrong here
More shards increase parallelism and could increase throttling if not needed.
- ✓
Increase the batch size in the Lambda event source mapping.
Why this is correct
Larger batch sizes mean fewer invocations, reducing throttling and errors.
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
Related to this question
About these practice questions
Courseiva writes every DEA-C01 question from scratch — 1,711 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DEA-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 DEA-C01 exam.