MLS-C01 Data Engineering Practice Question
A data engineering team needs to schedule a nightly ETL job that extracts data from an Amazon RDS for PostgreSQL instance, transforms it using Spark, and loads it into Amazon S3. The team wants to use AWS Glue for this task. Which components are required? (Select TWO.)
⚠ Common exam trap
Candidates often assume a crawler is mandatory for any Glue workflow, but the Data Catalog is only needed if you want to use it for schema discovery or as a metastore—the ETL job can operate without it by directly referencing the connection and writing raw data to S3.
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
✓
An AWS Glue ETL job with a Spark script.
An AWS Glue ETL job with a Spark script is required because the transformation step explicitly uses Spark. AWS Glue provides a managed Spark runtime, and the ETL job definition must include a script (either auto-generated or custom) that performs the extract, transform, and load operations. Without this component, the team cannot execute the Spark-based transformation logic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
An AWS Glue ETL job with a Spark script.
Why this is correct
The job performs the defined ETL logic.
- ✗
An AWS Glue crawler to populate the Data Catalog.
Why it's wrong here
Crawler is not required for the ETL job; schema can be defined manually.
- ✓
An AWS Glue connection to the RDS database.
Why this is correct
Connection enables Glue to access the database.
- ✗
An AWS Glue development endpoint.
Why it's wrong here
Development endpoint is for development, not production.
- ✗
An AWS Glue notebook for data exploration.
Why it's wrong here
Notebook is for exploration, not production scheduling.
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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