DEA-C01 Data Operations and Support Practice Question
A data engineer needs to transform a large dataset stored in Amazon S3 using Apache Spark. The engineer wants to minimize startup time and use a serverless approach. Which AWS service should the engineer use?
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
✓
AWS Glue
AWS Glue provides a serverless Spark environment with fast startup. Option A is wrong because Amazon Redshift is a data warehouse, not a Spark environment. Option B is wrong because Amazon EMR requires cluster provisioning, which increases startup time. Option D is wrong because Amazon Athena is for querying data, not for transforming with Spark.
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
Not a Spark environment.
- ✗
Amazon EMR
Why it's wrong here
Requires cluster provisioning, not serverless.
- ✓
AWS Glue
Why this is correct
Serverless Spark with fast startup.
- ✗
Amazon Athena
Why it's wrong here
For queries, not transformations.
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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