MLS-C01 Data Engineering Practice Question
A team is building a data pipeline using Amazon Kinesis Data Firehose to deliver real-time clickstream data to an Amazon S3 bucket. The data must be partitioned by year, month, day, and hour. Which configuration should the team use to achieve this?
⚠ Common exam trap
A common mix-up: candidates confuse S3 lifecycle rules or Athena partition projection as methods for creating partition structures, when in fact they are post-ingestion management or query-time features, not ingestion-time partitioning mechanisms.
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
✓
Enable dynamic partitioning in Firehose and configure the partition keys as YYYY/MM/dd/HH
Amazon Kinesis Data Firehose supports dynamic partitioning, which allows you to automatically partition incoming data in S3 based on keys like YYYY/MM/dd/HH. By enabling this feature and configuring the partition keys to match the desired year, month, day, and hour format, Firehose will write data directly into the corresponding S3 prefix structure without requiring additional processing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure an S3 lifecycle rule to move data into partition folders after delivery
Why it's wrong here
Lifecycle rules cannot reorganize data into partitions.
- ✗
Use an AWS Lambda function to write data to S3 with the desired partition structure
Why it's wrong here
Lambda adds cost and complexity; Firehose can do this natively.
- ✓
Enable dynamic partitioning in Firehose and configure the partition keys as YYYY/MM/dd/HH
Why this is correct
Firehose dynamic partitioning automatically creates folder structures.
- ✗
Use Amazon Athena partition projection to dynamically create partitions
Why it's wrong here
Partition projection is a feature of Athena, not applicable to Firehose delivery.
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.