MLS-C01 Data Engineering Practice Question
A company is using Amazon Kinesis Data Firehose to load streaming data into an S3 bucket. The data schema evolves over time, with new columns added. The data must be queryable using Amazon Athena. What is the BEST way to handle schema changes?
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 AWS Glue Crawlers to automatically detect schema changes and update the table metadata
AWS Glue Crawlers can automatically detect schema changes in the data stored in S3 and update the AWS Glue Data Catalog metadata used by Athena. This allows Athena to query the evolving schema without manual intervention. Option A (manual update) is not the best because it requires manual effort and is error-prone. Option B (converting to JSON) is not necessary; Athena can handle various formats including Parquet, ORC, etc., and schema evolution is better handled by Glue Crawlers. Option D (recreating the table daily) is disruptive and not the best practice.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually update the Athena table definition each time a new column is added
Why it's wrong here
Manual intervention is error-prone and not scalable.
- ✗
Configure Firehose to convert the data to Apache JSON format
Why it's wrong here
JSON is not required; Parquet or ORC with schema evolution is better.
- ✓
Use AWS Glue Crawlers to automatically detect schema changes and update the table metadata
Why this is correct
Glue Crawlers can run on a schedule to discover new columns and update the Data Catalog.
- ✗
Recreate the Athena table daily to pick up new columns
Why it's wrong here
Recreating tables can cause downtime and is inefficient.
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.