MLS-C01 Data Engineering Practice Question
A data scientist needs to run complex ETL transformations on a large dataset stored in Amazon S3. The transformations are written in PySpark and require occasional access to Hive metastore. The solution should minimize operational overhead and allow the data scientist to focus on code development. Which AWS service should be used?
⚠ Common exam trap
Test-takers frequently confuse AWS Glue's serverless Spark environment with the ability to run arbitrary PySpark code with Hive metastore access, but Glue abstracts away cluster management and does not provide the same level of control or direct Hive metastore integration as EMR.
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 EMR
Amazon EMR is the correct choice because it natively supports PySpark and Hive metastore integration, allowing the data scientist to run complex ETL transformations on large datasets stored in S3 with minimal operational overhead. EMR provides managed clusters that automatically scale and handle infrastructure, enabling the data scientist to focus on code development rather than cluster management.
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 Redshift
Why it's wrong here
Redshift is a data warehouse, not an ETL engine.
- ✓
Amazon EMR
Why this is correct
EMR provides a managed Spark environment with Hive support and allows custom PySpark code.
- ✗
AWS Glue
Why it's wrong here
Glue supports PySpark but has limitations on custom libraries and configurations.
- ✗
Amazon SageMaker
Why it's wrong here
SageMaker is for ML training and inference, not general-purpose ETL.
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.