- A
Amazon SageMaker Notebooks with custom Python scripts.
Why wrong: Notebooks are interactive, not automated for scheduled preprocessing.
- B
Amazon EMR with Spark clusters.
Why wrong: EMR requires cluster management, increasing operational overhead.
- C
AWS Glue ETL jobs reading from and writing to S3.
AWS Glue is serverless and designed for ETL on data lakes.
- D
Amazon Athena with SQL queries.
Why wrong: Athena is for querying, not for complex transformations like normalization.
Question 358 of 1,755
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon Athena with SQL queries.
Why it's wrong here
Athena is for querying, not for complex transformations like normalization.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
AWS Glue ETL jobs use Apache Spark under the hood, with automatic scaling based on the data volume and job parallelism defined by the number of DPUs (Data Processing Units). Glue also provides built-in transforms like `DropFields`, `Filter`, and `Join`, but for custom feature engineering, you can write Python or Scala code using the Glue DynamicFrame API, which handles schema inference and data partitioning from S3. A real-world scenario where this matters is processing terabytes of clickstream logs with nested JSON structures, where Glue's ability to handle schema evolution and automatically retry failed tasks reduces manual intervention.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
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