MLS-C01 Data Engineering Practice Question
A data engineer is designing an ETL pipeline using AWS Glue to process data from Amazon S3 and load it into Amazon Redshift. The pipeline must handle incremental data loads and ensure data consistency. Which THREE features should the engineer use to achieve this? (Choose THREE.)
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
✓
Pushdown predicates to filter partitions in S3
(pushdown predicates) filters S3 partitions, reducing data scanned and enabling efficient incremental loads. Option D (Redshift transactional tables with automatic commit) ensures data consistency during writes. Option E (Glue job bookmarks) tracks processed data, supporting incremental processing. Option B (Glue data preview) is used for development and does not contribute to incremental loading or consistency. Option C (Glue partition filters) is less efficient than pushdown predicates for filtering partitions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Pushdown predicates to filter partitions in S3
Why this is correct
Pushdown predicates reduce the amount of data read from S3, improving performance.
- ✗
Glue data preview to validate transformation logic
Why it's wrong here
Data preview is for development, not production consistency.
- ✗
Glue partition filters to limit data scanned
Why it's wrong here
Partition filters are useful but not specifically for incremental loads; pushdown predicates are better.
- ✓
Redshift transactional tables with automatic commit
Why this is correct
Transactional tables ensure consistency during concurrent writes.
- ✓
Glue job bookmarks to track processed data
Why this is correct
Job bookmarks enable incremental processing by remembering previously processed data.
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.