MLS-C01 Data Engineering Practice Question
A company needs to perform complex transformations on large datasets stored in Amazon S3 using Apache Spark. They want to minimize operational overhead. Which AWS service should they use?
⚠ Common exam trap
It's easy for candidates to confuse AWS Glue's Spark-based ETL engine with a full-fledged Spark cluster, overlooking that Glue is optimized for simpler, serverless ETL jobs and lacks the fine-grained control and performance tuning capabilities of Amazon EMR for complex transformations.
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 is a managed big data platform that natively runs Apache Spark, allowing you to perform complex transformations on large datasets stored in Amazon S3 without provisioning or managing underlying infrastructure. EMR automatically handles cluster lifecycle, scaling, and tuning, minimizing operational overhead while providing full Spark compatibility.
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 EMR
Why this is correct
EMR provides managed Spark clusters for complex transformations.
- ✗
Amazon EC2 with manually configured Spark
Why it's wrong here
Increases operational overhead.
- ✗
Amazon Athena
Why it's wrong here
Athena is for SQL queries, not Spark.
- ✗
AWS Glue
Why it's wrong here
Glue is best for simple ETL, not complex Spark jobs.
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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