DEA-C01 Data Ingestion and Transformation Practice Question
A data engineering team is responsible for ingesting streaming data from a fleet of IoT devices into Amazon S3 using Kinesis Data Firehose. The data volume spikes unpredictably, and the team has configured Kinesis Data Firehose with a buffer size of 5 MB and buffer interval of 60 seconds. During spikes, the team notices that the delivery to S3 is delayed, and some records are lost due to exceeding the service limits. The team needs to ensure no data loss and reduce delivery latency. What should the team do?
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
✓
Use Amazon Kinesis Data Streams as the data source for Firehose to decouple ingestion and delivery.
Using Kinesis Data Streams as the data source for Firehose decouples ingestion and delivery. The stream acts as a durable buffer that can absorb unpredictable spikes, preventing data loss due to Firehose service limits. Firehose can then be configured with smaller buffer size/interval to reduce delivery latency, as the stream retains data until successful delivery. Option B increases buffer size and interval, which would increase latency, contradicting the requirement. Option A adds complexity and does not directly address buffering. Option D is unrelated to Firehose buffering.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Implement an AWS Lambda function to pre-process the data and send it to Firehose in a throttled manner.
Why it's wrong here
Implementing a Lambda function to pre-process and throttle data adds latency and complexity; throttling could still cause data loss during extreme spikes. This does not resolve the underlying buffer configuration issue.
- ✗
Increase the buffer size to 10 MB and buffer interval to 120 seconds to allow more data accumulation before delivery.
Why it's wrong here
Increasing buffer size to 10 MB and interval to 120 seconds would increase delivery latency, not reduce it. While it may reduce API calls, it does not meet the requirement to reduce latency and may still risk data loss if limits are exceeded.
- ✓
Use Amazon Kinesis Data Streams as the data source for Firehose to decouple ingestion and delivery.
Why this is correct
Using Kinesis Data Streams as the data source decouples ingestion from delivery, providing a durable buffer that absorbs spikes. Firehose can be configured with smaller buffer settings for lower latency, and data is retained in the stream until delivered, preventing data loss.
- ✗
Enable S3 Transfer Acceleration on the destination bucket.
Why it's wrong here
S3 Transfer Acceleration improves network transfer speeds from clients to S3 but does not affect Firehose's internal buffering or delivery behavior. It does not address the latency or data loss issue.
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.