MLA-C01 Data Preparation for Machine Learning Practice Question
A data engineer needs to prepare a large dataset (10 TB) stored in Amazon S3 for a training job on SageMaker. The data is in CSV format, but the training algorithm expects Parquet for performance. The engineer must transform the data with minimal cost and without writing custom code. Which service should be used?
⚠ Common exam trap
Test-takers frequently confuse Amazon S3 Select's ability to filter data with the ability to transform data formats, but S3 Select only returns filtered results in the original format and cannot perform format conversion like CSV to Parquet.
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 to create a crawler and ETL job that converts CSV to Parquet.
AWS Glue is the correct choice because it provides a serverless, pay-per-use ETL service that can automatically convert CSV to Parquet without writing custom code. The Glue crawler infers the schema, and the ETL job uses built-in transforms to efficiently handle 10 TB of data with minimal cost, as it only charges for the resources consumed during the job execution.
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 AWS Glue to create a crawler and ETL job that converts CSV to Parquet.
Why this is correct
Glue offers a serverless, code-free option for format conversion.
- ✗
Use SageMaker Processing with a TensorFlow script to read CSV and write Parquet.
Why it's wrong here
This requires writing custom code and provisioning resources.
- ✗
Use Amazon S3 Select to convert the data to Parquet during retrieval.
Why it's wrong here
S3 Select only supports filtering, not format conversion.
- ✗
Use Amazon EMR with a Spark job to convert the files.
Why it's wrong here
EMR requires cluster management and custom code, which the engineer wants to avoid.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.