- A
Amazon Kinesis Data Firehose with data transformation
Why wrong: Firehose transformations are limited to Lambda functions with constraints; not ideal for complex enrichment.
- B
AWS Lambda functions invoked by Kinesis Data Streams
Why wrong: Lambda has execution time limits and may not efficiently handle high throughput or stateful processing.
- C
Amazon Kinesis Data Analytics for Apache Flink
Managed Flink application can perform complex transformations and enrichments with low operational overhead.
- D
Amazon EMR with Apache Spark Streaming
Why wrong: EMR requires cluster provisioning and management, increasing overhead.
Question 860 of 1,755
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon EMR with Apache Spark Streaming
Why it's wrong here
EMR requires cluster provisioning and management, increasing overhead.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Kinesis Data Analytics for Apache Flink uses the Apache Flink runtime under the hood, which supports event-time processing, exactly-once semantics, and stateful operators like RichAsyncFunction for non-blocking DynamoDB lookups. The service automatically manages Flink's checkpointing to S3 for fault tolerance and scales the parallelism of the Flink job based on the Kinesis shard count, eliminating the need for manual infrastructure management. In a real-world scenario, this allows enrichment of clickstream events with user profile data from DynamoDB at thousands of records per second without data loss or duplication.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
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