MLS-C01 Data Engineering Practice Question
A company uses Amazon Kinesis Data Firehose to deliver data to Amazon S3. The data must be transformed before delivery using AWS Lambda. The Lambda function adds a timestamp field. The Firehose stream receives up to 10,000 records per second. The transformation currently takes 500 ms per record. What should the team do to ensure the transformation can keep up with the incoming data without data loss?
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 Lambda concurrency limit for the function to handle parallel invocations.
Increasing the Lambda concurrency limit allows more parallel invocations, enabling the function to process the high throughput of 10,000 records per second (each taking 500 ms) without falling behind. Option A is incorrect because shards are a concept for Kinesis Data Streams, not Firehose. Option B is incorrect: placing the Lambda function in a VPC typically adds network latency and does not improve performance for this simple transformation. Option D is incorrect: increasing S3 buffer size/interval may delay data delivery but does not increase transformation capacity.
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 number of shards in the Kinesis stream.
Why it's wrong here
Incorrect. Shards are associated with Kinesis Data Streams, not Firehose. Firehose automatically scales to handle throughput.
- ✗
Place the Lambda function in a VPC to improve network performance.
Why it's wrong here
Incorrect. Placing the Lambda function in a VPC can introduce additional latency and is unnecessary for adding a timestamp; it does not help with throughput.
- ✓
Increase the Lambda concurrency limit for the function to handle parallel invocations.
Why this is correct
Correct. Increasing the Lambda concurrency limit allows multiple instances of the function to run in parallel, enabling it to keep up with the high record rate and preventing data loss.
- ✗
Increase the S3 buffer size and buffer interval in the Firehose delivery stream.
Why it's wrong here
Incorrect. Increasing the S3 buffer size or interval only affects how often data is written to S3, not the transformation speed. It may lead to increased latency or data loss if the buffer fills up.
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
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