MLS-C01 Data Engineering Practice Question
A machine learning team needs to preprocess large volumes of clickstream data stored in Amazon S3 before training a model. The preprocessing includes data cleaning, feature engineering, and normalization. The team wants to use a serverless solution that minimizes operational overhead. Which combination of services should the team use?
⚠ Common exam trap
It's easy for candidates to confuse 'serverless' with 'managed' — EMR is managed but not serverless, while Athena is serverless but lacks the flexibility for complex ETL transformations, leading them to incorrectly choose Athena or 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
✓
AWS Glue ETL jobs reading from and writing to S3.
AWS Glue ETL jobs are a serverless solution that automatically provisions and scales the underlying compute resources, making them ideal for preprocessing large volumes of clickstream data stored in S3. Glue can read directly from S3, perform data cleaning, feature engineering, and normalization using PySpark or Scala, and write the transformed data back to S3, all without managing any infrastructure. This minimizes operational overhead while handling the required preprocessing tasks at scale.
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 SageMaker Notebooks with custom Python scripts.
Why it's wrong here
Notebooks are interactive, not automated for scheduled preprocessing.
- ✗
Amazon EMR with Spark clusters.
Why it's wrong here
EMR requires cluster management, increasing operational overhead.
- ✓
AWS Glue ETL jobs reading from and writing to S3.
Why this is correct
AWS Glue is serverless and designed for ETL on data lakes.
- ✗
Amazon Athena with SQL queries.
Why it's wrong here
Athena is for querying, not for complex transformations like normalization.
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
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