DEA-C01 Data Ingestion and Transformation Practice Question
A financial company needs to ingest real-time stock trade data from multiple sources and store it in Amazon S3 for compliance. The data must be delivered within 1 minute of the trade occurring. The data volume is approximately 10,000 records per second, with occasional spikes to 50,000 records per second. The engineer has set up Amazon Kinesis Data Streams with 10 shards and a Kinesis Data Firehose delivery stream that reads from the Kinesis stream and writes to S3. However, during spikes, the Firehose delivery stream falls behind, causing data to be delayed beyond the 1-minute SLA. What should the engineer do to meet the SLA without over-provisioning?
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 number of shards in the Kinesis data stream to handle peak throughput and enable auto-scaling.
Increasing the number of shards in the Kinesis data stream allows it to handle the peak throughput of 50,000 records per second, and enabling auto-scaling ensures the stream adapts to varying loads without manual intervention. This reduces the backlog that causes Firehose to fall behind, meeting the 1-minute SLA. Option A is incorrect because increasing the buffer size in Firehose would increase latency, not reduce it. Option B is incorrect because adding SQS as a buffer adds another hop, increasing latency and complexity. Option C is incorrect because AWS Lambda may not scale to 50,000 records per second and adds processing latency.
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 buffer size in Kinesis Data Firehose from 1 MB to 5 MB to batch more data per delivery.
Why it's wrong here
Larger buffers increase latency, not reduce it.
- ✗
Use Amazon SQS as a buffer between Kinesis and Firehose to absorb spikes.
Why it's wrong here
Adding SQS introduces additional latency and complexity.
- ✗
Replace Firehose with an AWS Lambda function that writes directly to S3 for lower latency.
Why it's wrong here
Lambda may throttle during spikes and cannot handle the throughput without careful configuration.
- ✓
Increase the number of shards in the Kinesis data stream to handle peak throughput and enable auto-scaling.
Why this is correct
More shards increase read capacity; auto-scaling adjusts during spikes.
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 |
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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.