MLS-C01 Data Engineering Practice Question
A retail company uses Amazon Redshift for its data warehouse. The data engineering team runs ETL jobs that load data from multiple sources into Redshift daily. They notice that the load performance is slow and the cluster CPU utilization is high during the ETL window. The team wants to improve load performance without changing the cluster configuration. They currently load data using INSERT statements from a staging table. What should they do?
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 the COPY command to load data from S3 in parallel
The COPY command is the most efficient way to load large amounts of data into Amazon Redshift because it uses the cluster's nodes in parallel to read data from Amazon S3, maximizing throughput. Option A (VACUUM and ANALYZE) are maintenance operations that reclaim space and update statistics, but they do not improve load performance. Option C (increasing node count) contradicts the requirement to not change cluster configuration. Option D (compression encoding) can reduce storage and improve scan performance but does not significantly speed up the initial load.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Run VACUUM and ANALYZE before loading
Why it's wrong here
VACUUM is for sorting, not load performance.
- ✓
Use the COPY command to load data from S3 in parallel
Why this is correct
COPY is optimized for bulk loading.
- ✗
Increase the number of nodes in the Redshift cluster
Why it's wrong here
Changes cluster configuration.
- ✗
Apply compression encoding on the staging table
Why it's wrong here
Compression helps storage but not load speed significantly.
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.