- A
Use SageMaker Processing jobs to run custom scripts.
Why wrong: SageMaker Processing jobs require infrastructure provisioning and are more suited for ML-specific tasks, not general ETL.
- B
Use Amazon Athena to query and transform the data.
Why wrong: Athena is for interactive querying, not complex ETL transformations with nested JSON parsing and joins.
- C
Use Amazon EMR with Apache Spark.
Why wrong: EMR provides powerful Spark capabilities but requires cluster management, increasing infrastructure overhead.
- D
Use AWS Glue ETL with PySpark.
Correct. Glue ETL with PySpark is serverless, scales automatically, and handles complex transformations with minimal management.
Question 47 of 1,000
MLA-C01 AWS Glue ETL Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. 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. A key principle to apply: aWS Glue ETL. 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 company has 10 TB of log data in compressed JSON format stored in Amazon S3. The data needs to be processed and transformed into a structured format for machine learning. The processing requires complex transformations, including parsing nested JSON and joining with a reference table. The company wants to minimize infrastructure management. Which approach should the company use?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 AWS Glue ETL with PySpark.
AWS Glue ETL with PySpark (Option D) is the best choice because it provides a fully serverless environment, minimizing infrastructure management. Glue can handle complex transformations like parsing nested JSON and joining with reference tables using PySpark, and it scales automatically for large datasets (10 TB). Amazon EMR (Option C) requires cluster management and provisioning, which contradicts the goal of minimizing management overhead.
Key principle: AWS Glue ETL
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use SageMaker Processing jobs to run custom scripts.
Why it's wrong here
SageMaker Processing jobs require infrastructure provisioning and are more suited for ML-specific tasks, not general ETL.
- ✗
Use Amazon Athena to query and transform the data.
Why it's wrong here
Athena is for interactive querying, not complex ETL transformations with nested JSON parsing and joins.
- ✗
Use Amazon EMR with Apache Spark.
Why it's wrong here
EMR provides powerful Spark capabilities but requires cluster management, increasing infrastructure overhead.
- ✓
Use AWS Glue ETL with PySpark.
Why this is correct
Correct. Glue ETL with PySpark is serverless, scales automatically, and handles complex transformations with minimal management.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
AWS Glue ETL
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates may assume that large-scale data (10 TB) requires a provisioned cluster like EMR, but AWS Glue can scale to petabyte-scale workloads and is fully serverless, aligning with the goal of minimizing infrastructure management.
Detailed technical explanation
How to think about this question
Under the hood, Apache Spark on EMR uses Resilient Distributed Datasets (RDDs) and DataFrames to distribute data across nodes, enabling parallel processing of nested JSON via `from_json()` and `explode()` functions. The reference table join can be optimized using broadcast joins if the table is small, or sort-merge joins for larger tables, leveraging Spark's Catalyst optimizer. In real-world scenarios, EMR's ability to use spot instances for cost savings and its integration with S3 via the EMRFS connector make it a robust choice for petabyte-scale ETL pipelines.
KKey Concepts to Remember
- AWS Glue ETL
- Infrastructure Management
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
AWS Glue ETL
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.
Review aWS Glue ETL, then practise related MLA-C01 questions on the same topic to reinforce the concept.
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Data Preparation for Machine Learning — study guide chapter
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — AWS Glue ETL.
What is the correct answer to this question?
The correct answer is: Use AWS Glue ETL with PySpark. — AWS Glue ETL with PySpark (Option D) is the best choice because it provides a fully serverless environment, minimizing infrastructure management. Glue can handle complex transformations like parsing nested JSON and joining with reference tables using PySpark, and it scales automatically for large datasets (10 TB). Amazon EMR (Option C) requires cluster management and provisioning, which contradicts the goal of minimizing management overhead.
What should I do if I get this MLA-C01 question wrong?
Review aWS Glue ETL, then practise related MLA-C01 questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
AWS Glue ETL
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Last reviewed: Jun 24, 2026
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