DEA-C01 Data Ingestion and Transformation Practice Question
A data engineering team is ingesting streaming data from IoT devices into Amazon Kinesis Data Streams. The data is then consumed by an AWS Lambda function that transforms and loads it into Amazon S3. Recently, the team noticed that the Lambda function is failing with throttling errors (HTTP 429) from the Kinesis API. Which configuration change should the team make to resolve this issue?
⚠ Common exam trap
A common mix-up: candidates assume increasing the batch size reduces invocations and thus throttling, but in reality, larger batches increase the data volume per GetRecords call, making throttling worse; the correct approach is to reduce batch size and increase shards to distribute the read load.
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
✓
Reduce the batch size and increase the number of shards in the Kinesis stream to increase parallelism.
Reducing the batch size and increasing the number of shards directly addresses the HTTP 429 throttling errors from the Kinesis API. Each shard supports up to 5 read transactions per second and a maximum read rate of 2 MB/s; by increasing shards, you increase the available read throughput, and reducing the batch size lowers the number of records per invocation, preventing the Lambda function from exceeding the per-shard read limits.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable retries on the Lambda function and configure a dead-letter queue for failed records.
Why it's wrong here
Disabling retries would lose data; dead-letter queue handles failures but does not prevent throttling.
- ✗
Replace Kinesis Data Streams with Amazon DynamoDB Streams for ingestion.
Why it's wrong here
DynamoDB Streams is for database changes, not IoT ingestion, and does not solve the throttling issue.
- ✓
Reduce the batch size and increase the number of shards in the Kinesis stream to increase parallelism.
Why this is correct
Reducing batch size lowers records per invocation, and more shards increase parallelism, reducing throttling.
- ✗
Increase the batch size in the Lambda event source mapping to reduce the number of invocations.
Why it's wrong here
Increasing batch size increases the number of records per invocation, likely causing more throttling.
Visual reference
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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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.