MLA-C01 Practice Question: A company ingests streaming transaction data from…

A company ingests streaming transaction data from multiple sources using Amazon Kinesis Data Streams. The data must be transformed (e.g., JSON parsing, data type conversions) and then stored in Amazon S3 for ML training. The transformation logic may change over time. Which approach provides the greatest flexibility and ease of maintenance?

⚠ Common exam trap

MLA-C01 often tests whether candidates pick the most 'powerful' streaming service — the trap is choosing Kinesis Data Analytics or a custom EC2 consumer when the requirement is flexible, low-maintenance transformation and S3 delivery, which Firehose with Lambda handles natively.

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 Kinesis Data Firehose with a Lambda function for transformation and delivery to S3.

Kinesis Data Firehose with a Lambda function for transformation gives you a fully managed delivery pipeline to S3 while letting you change transformation logic by updating the Lambda code, with no servers to manage. This satisfies both the flexibility (logic can evolve) and ease-of-maintenance requirements.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Use a custom application running on Amazon EC2 with the Kinesis Client Library to transform and write to S3.
Why it's wrong here
A custom EC2 application with the Kinesis Client Library requires the engineer to provision, patch and scale servers, and redeploy code for every transformation change, adding operational overhead. This approach suits bespoke processing logic that managed services cannot express, not a requirement centred on flexibility and ease of maintenance.
✓
Use Kinesis Data Firehose with a Lambda function for transformation and delivery to S3.
Why this is correct
Kinesis Data Firehose with a Lambda function decouples transformation logic from delivery, so engineers can update the Lambda code without rebuilding the pipeline. This satisfies the requirement that transformation logic may change over time while still landing data in Amazon S3.
✗
Use Kinesis Data Analytics with SQL queries to transform data and output to S3.
Why it's wrong here
Kinesis Data Analytics with SQL restricts transformations to SQL semantics, so complex JSON parsing and type conversions become awkward and logic changes mean rewriting queries. It is chosen for continuous SQL analytics over streams, not for maintainable, evolving transformation pipelines feeding S3.
✗
Use Kinesis Data Streams directly with a Lambda consumer to transform and store in S3.
Why it's wrong here
A Lambda consumer on Kinesis Data Streams requires managing shard iterators, checkpointing, error retries and scaling, so transformation changes mean redeploying and retesting code. It is chosen for lightweight custom processing, whereas managed delivery to S3 with configurable transforms better meets the flexibility requirement.

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

MLA-C01 Practice Question: A company ingests streaming transaction data from…

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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