MLS-C01 Data Engineering Practice Question
A company uses Amazon Kinesis Data Firehose to ingest streaming data and deliver it to an S3 bucket. The data is in JSON format with a timestamp field. The data science team wants to query the data using Athena with partitioning by year/month/day. How should the S3 data be organized?
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
✓
Configure Firehose to use dynamic partitioning with custom prefix
Kinesis Firehose can partition data using custom prefixes like 'year=!{timestamp:yyyy}/month=!{timestamp:MM}/day=!{timestamp:dd}/'. This creates Hive-style partitions that Athena can automatically discover.
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 Firehose to use dynamic partitioning with custom prefix
Why this is correct
Firehose dynamic partitioning creates directories based on record fields or timestamps.
- ✗
Store data in a single prefix and use Athena's 'partition projection' feature
Why it's wrong here
Partition projection works but requires manual configuration; dynamic partitioning is more automated.
- ✗
Use AWS Glue crawler to partition the data after delivery
Why it's wrong here
While a crawler can partition, it's better to partition at ingestion to avoid additional processing.
- ✗
Use Amazon EMR to partition the data after delivery
Why it's wrong here
EMR adds complexity and cost; not needed when Firehose can partition directly.
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 |
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1 more way this is tested on MLS-C01
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Variation 1. A company uses Amazon Kinesis Data Firehose to deliver streaming data to an S3 bucket. The data is JSON and must be partitioned by year, month, and day. The delivery stream is configured with a buffer interval of 60 seconds and buffer size of 5 MB. The data producer sends about 1 MB per second. The data is arriving in S3 but the partitions are not being created as expected. What is the MOST likely reason?
medium- A.The data is encrypted with AWS KMS and Firehose cannot write to encrypted buckets.
- ✓ B.The delivery stream does not have dynamic partitioning enabled with the appropriate custom prefix.
- C.The buffer interval is too short for the data volume, causing incomplete records.
- D.The S3 bucket has versioning enabled, which prevents partitioning.
Why B: Kinesis Data Firehose requires dynamic partitioning to be explicitly enabled and configured with a custom prefix (e.g., 'year=!{timestamp:yyyy}/month=!{timestamp:MM}/day=!{timestamp:dd}/') to automatically partition data by year, month, and day. Without this setting, Firehose writes all data to a single S3 prefix, ignoring the desired partition structure.
JA
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.