MLS-C01 Data Engineering Practice Question
A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data must be transformed before being stored in Amazon S3. The transformations include enrichment with reference data from Amazon DynamoDB. Which AWS service should be used to perform the transformation with minimal operational overhead?
⚠ Common exam trap
It's easy for candidates to choose Kinesis Data Firehose (Option A) because it directly integrates with S3 and DynamoDB via Lambda, but they overlook that Firehose cannot perform stateful joins or handle reference data enrichment at scale without complex custom code, whereas Kinesis Data Analytics for Apache Flink is purpose-built for exactly this pattern with minimal operational overhead.
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
✓
Amazon Kinesis Data Analytics for Apache Flink
Amazon Kinesis Data Analytics for Apache Flink (Option C) is the correct choice because it provides a fully managed, stateful stream processing engine that can read directly from Kinesis Data Streams, enrich records with reference data from DynamoDB via Flink's Async I/O or JDBC connectors, and write the transformed data to S3—all without provisioning or managing any infrastructure. This minimizes operational overhead compared to self-managed solutions like EMR or Lambda-based architectures that require custom checkpointing and scaling logic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon Kinesis Data Firehose with data transformation
Why it's wrong here
Firehose transformations are limited to Lambda functions with constraints; not ideal for complex enrichment.
- ✗
AWS Lambda functions invoked by Kinesis Data Streams
Why it's wrong here
Lambda has execution time limits and may not efficiently handle high throughput or stateful processing.
- ✓
Amazon Kinesis Data Analytics for Apache Flink
Why this is correct
Managed Flink application can perform complex transformations and enrichments with low operational overhead.
- ✗
Amazon EMR with Apache Spark Streaming
Why it's wrong here
EMR requires cluster provisioning and management, increasing overhead.
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 MLS-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 MLS-C01 exam.