MLS-C01 Data Engineering Practice Question
A company uses AWS Glue to run ETL jobs that process data from Amazon RDS for PostgreSQL and load it into Amazon Redshift. The Glue job runs nightly and takes 6 hours to complete. The Redshift cluster is a single dc2.large node. The team needs to reduce the load time to under 3 hours. The data volume is 200 GB per night. The team is considering using Amazon Redshift Spectrum to query data directly from S3 instead of loading it. However, the data transformation logic is complex and requires multiple joins and aggregations that are currently performed in Glue. Which approach should the team recommend to meet the time requirement?
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
✓
Increase the Redshift cluster to a multi-node cluster with dc2.8xlarge nodes to improve COPY and query performance.
Increasing the Redshift cluster to a multi-node configuration with dc2.8xlarge nodes significantly improves COPY performance by parallelizing data loading across multiple slices. The current single dc2.large node may be the bottleneck for the data loading portion of the Glue job. Faster loading can reduce the total 6-hour runtime to under 3 hours if loading is the dominant factor. Note: the complex transformations remain in AWS Glue, not on Redshift. Option A is incorrect because Redshift Spectrum allows querying external tables without loading but does not accelerate the complex ETL transformations. Option C splits the load but does not address the single-node ingestion bottleneck and adds complexity. Option D improves load speed with Parquet but the cluster's ingestion capacity is still the limiting factor.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Redshift Spectrum to create external tables and run the transformations directly in Redshift, bypassing the Glue job.
Why it's wrong here
Redshift Spectrum can query S3 but the complex transformations would still need to be executed, potentially slower than Glue.
- ✓
Increase the Redshift cluster to a multi-node cluster with dc2.8xlarge nodes to improve COPY and query performance.
Why this is correct
More nodes increase parallelism for loading and any post-load transformations.
- ✗
Split the Glue job into multiple parallel jobs that each load a portion of the data into separate Redshift tables, then use UNION ALL views.
Why it's wrong here
This adds complexity and does not necessarily reduce total time if the Redshift cluster is the bottleneck.
- ✗
Stage the data in S3 in Parquet format and use a COPY command with the PARQUET option to load data faster.
Why it's wrong here
This reduces load time but the transformation time in Glue remains the same.
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.