MLA-C01 Data Preparation for Machine Learning Practice Question
A data engineer needs to convert a JSON dataset to Parquet format for efficient querying with Amazon Athena. The JSON files are in an S3 bucket. Which service can perform this conversion with minimal coding?
⚠ Common exam trap
Many candidates confuse AWS Glue Studio with AWS Glue DataBrew or assume that any AWS service with 'processing' in its name (like SageMaker Processing) is suitable for simple ETL tasks, overlooking the specific no-code visual job capability of Glue Studio.
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 Studio with a visual job
AWS Glue Studio with a visual job is the correct choice because it provides a no-code, drag-and-drop interface to create ETL jobs that can read JSON from S3 and write it as Parquet, with built-in schema inference and transformation capabilities. This minimizes coding effort while leveraging Glue's serverless Spark engine for efficient conversion, making it ideal for preparing data for Athena queries.
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 Processing
Why it's wrong here
SageMaker Processing is optimized for ML preprocessing, not simple file format conversion.
- ✗
Amazon EMR
Why it's wrong here
While Amazon EMR can convert JSON to Parquet using Spark or Hive, it requires writing and managing a script or notebook, failing the "minimal coding" constraint of the question. It is tempting because EMR is a common choice for large-scale ETL on semi-structured data, and would be correct if the engineer needed to perform complex transformations or run custom logic beyond a simple format conversion.
- ✗
AWS Lambda
Why it's wrong here
Lambda is serverless but requires writing code and handling large files may be complex.
- ✓
AWS Glue Studio with a visual job
Why this is correct
Glue Studio's drag-and-drop interface enables JSON to Parquet conversion with minimal coding.
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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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.