DEA-C01 Data Operations and Support Practice Question
A data engineering team notices that an Amazon Kinesis Data Stream is frequently exceeding its shard write throughput limit, causing throttling. The team needs a long-term solution to handle variable write traffic without manual intervention. Which action should the team 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
✓
Implement a buffer using Amazon S3 and AWS Lambda that aggregates records and writes to Kinesis in batches.
Using Amazon S3 and AWS Lambda to buffer and batch records before writing to Kinesis smooths out traffic spikes, reducing throttling without manual intervention. Option A is incorrect because the Kinesis Client Library (KCL) is used by consumers, not producers; it cannot throttle write throughput. Option B is incorrect because manually increasing shards during peak hours requires manual intervention and is not a long-term automated solution. Option C is incorrect because Amazon Kinesis Data Firehose is designed to deliver streaming data to destinations like S3, Redshift, or Elasticsearch, not to buffer records before writing to a Kinesis stream.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure the Kinesis Client Library to throttle consumption.
Why it's wrong here
The KCL is for consumers, not producers.
- ✗
Increase the number of shards manually during peak hours.
Why it's wrong here
Manual scaling does not handle variable traffic automatically.
- ✗
Use Amazon Kinesis Data Firehose to buffer records before delivery to the stream.
Why it's wrong here
Firehose delivers to destinations like S3, not Kinesis streams.
- ✓
Implement a buffer using Amazon S3 and AWS Lambda that aggregates records and writes to Kinesis in batches.
Why this is correct
This buffers writes and reduces throttling.
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 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.