MLS-C01 Data Engineering Practice Question
A data engineering team needs to set up a data pipeline that ingests streaming data from an Apache Kafka cluster running on Amazon EKS into an S3 data lake. The data must be stored in Parquet format, partitioned by date and event type. The team wants a fully managed solution with minimal operational overhead. Which solution should they choose?
⚠ Common exam trap
A common mix-up: candidates confuse Kinesis Data Firehose's ability to accept data from various sources with direct Kafka integration, but Firehose does not natively support Kafka as a source without additional services like Kinesis Data Streams or a custom producer.
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 Amazon MSK (Managed Streaming for Apache Kafka) and configure an MSK Connect S3 sink connector.
Amazon MSK is a fully managed Apache Kafka service that integrates with MSK Connect, which provides a pre-built S3 sink connector. This connector can directly stream data from Kafka topics to S3 in Parquet format with partitioning by date and event type, requiring no custom code or infrastructure management. This minimizes operational overhead while meeting all requirements for a fully managed solution.
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 Amazon MSK (Managed Streaming for Apache Kafka) and configure an MSK Connect S3 sink connector.
Why this is correct
MSK is fully managed Kafka, and MSK Connect can stream data to S3 in Parquet format.
- ✗
Set up a Kinesis Data Firehose delivery stream that reads from Kafka and writes to S3.
Why it's wrong here
Firehose cannot read directly from Kafka; it requires a Kinesis stream or other sources.
- ✗
Use AWS Glue ETL jobs to pull data from Kafka cluster periodically.
Why it's wrong here
Glue ETL is batch-oriented and not designed for streaming ingestion.
- ✗
Create a Kinesis Data Analytics application to read from Kafka and write to S3.
Why it's wrong here
Kinesis Data Analytics is for real-time analytics, not for direct data lake ingestion.
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.