Question 985 of 1,672
MLS-C01 Data Engineering Practice Question
A data engineer needs to transform raw clickstream data (JSON files) stored in S3 into a partitioned Parquet dataset for querying with Athena. The transformation includes cleaning, deduplication, and enrichment. The pipeline should run daily. Which solution is MOST cost-effective and requires the least operational overhead?
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
✓
Use an AWS Glue ETL job with a schedule trigger to perform the transformation and write to S3.
AWS Glue ETL jobs are serverless, require no cluster management, and can be easily scheduled for daily runs. Glue also integrates with the Data Catalog for partitioning. Option A (Amazon EMR) is not the most cost-effective or least operational overhead because it requires managing a cluster, even if it can be terminated after completion. Option C (AWS Lambda) is not suitable for large-scale clickstream data due to execution time limits and lack of built-in support for complex transformations like deduplication. Option D (Amazon Athena CTAS) is not appropriate because Athena is primarily a query engine, not a transformation tool; CTAS queries are good for converting data formats but lack the flexibility for cleaning and enrichment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Launch an Amazon EMR cluster with Spark, transform the data, and terminate the cluster after completion.
Why it's wrong here
EMR requires provisioning and managing clusters; Glue offers lower operational overhead for scheduled jobs.
- ✓
Use an AWS Glue ETL job with a schedule trigger to perform the transformation and write to S3.
Why this is correct
Glue ETL is serverless, can handle complex transformations, and scheduling is built-in.
- ✗
Use AWS Lambda functions triggered by S3 events to transform each file incrementally.
Why it's wrong here
Lambda is limited to 15-minute execution and 512 MB disk, unsuitable for large-scale daily transformations.
- ✗
Use Amazon Athena to run CTAS queries to convert and partition the data daily.
Why it's wrong here
Athena CTAS is convenient but charges per scan; for daily runs, Glue ETL is more cost-effective and offers more transformation capabilities.
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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Last reviewed: Jun 20, 2026
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