MLS-C01 Data Engineering Practice Question
A data scientist needs to transform raw JSON data from an S3 bucket into Parquet format using AWS Glue. The job must be cost-effective and run only when new data arrives. Which solution should be used?
⚠ Common exam trap
It's easy for candidates to confuse Glue crawlers (which only catalog metadata) with Glue ETL jobs (which transform data), or assume scheduled jobs are always cost-effective without considering event-driven triggers.
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
✓
Create a Glue ETL job triggered by an S3 event notification via Lambda.
It uses an S3 event notification to invoke a Lambda function, which then triggers an AWS Glue ETL job only when new data arrives. This event-driven architecture ensures cost-effectiveness by avoiding continuous or scheduled runs, and it directly transforms raw JSON into Parquet format as required.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a Glue crawler that runs continuously.
Why it's wrong here
Crawlers discover schema, not transform data.
- ✗
Schedule a Glue ETL job to run every hour.
Why it's wrong here
Scheduled runs may waste resources if no new data.
- ✗
Use Glue DataBrew to transform data and schedule it daily.
Why it's wrong here
DataBrew is interactive, not automated event-driven.
- ✓
Create a Glue ETL job triggered by an S3 event notification via Lambda.
Why this is correct
Event-driven trigger ensures cost-effectiveness.
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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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.