MLS-C01 Data Engineering Practice Question
A team wants to build a data pipeline that processes incoming JSON files from an S3 bucket and loads them into a Redshift table. The pipeline must handle schema evolution and data validation. Which combination of services would be MOST appropriate?
⚠ Common exam trap
A common mix-up: candidates choose AWS Lambda for its simplicity and event-driven nature, overlooking its limitations with large files, lack of schema evolution, and inability to perform complex ETL within execution constraints.
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
✓
Amazon S3 + AWS Glue + Amazon Redshift
AWS Glue provides built-in schema discovery and evolution capabilities via its crawlers and the Data Catalog, which automatically detect and adapt to changes in JSON schemas. Combined with Glue ETL jobs for data validation and transformation, it seamlessly loads processed data into Amazon Redshift, making it the most appropriate choice for handling schema evolution and validation in this pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Amazon S3 + AWS Glue + Amazon Redshift
Why this is correct
Glue provides schema inference and ETL.
- ✗
Amazon S3 + Amazon SQS + Amazon Redshift
Why it's wrong here
SQS is for decoupling, not for ETL.
- ✗
Amazon S3 + AWS Data Pipeline + Amazon Redshift
Why it's wrong here
Data Pipeline is less integrated for schema evolution.
- ✗
Amazon S3 + AWS Lambda + Amazon Redshift
Why it's wrong here
Lambda lacks native schema evolution capabilities.
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.