MLA-C01 Data Preparation for Machine Learning Practice Question
A data engineer needs to prepare a large dataset for machine learning. The data is stored in an Amazon RDS MySQL database and needs to be transformed and moved to an S3 bucket in Parquet format for use with SageMaker. Which AWS service is most suitable for this extraction, transformation, and loading (ETL) task?
⚠ Common exam trap
Many exam-takers confuse SageMaker Data Wrangler's ability to connect to RDS and export data with a full ETL capability, overlooking that it is an interactive tool for data preparation within SageMaker Studio rather than a serverless batch ETL service like AWS Glue.
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 jobs with PySpark to read from RDS, apply transformations, and write to S3 as Parquet.
AWS Glue ETL jobs with PySpark are the most suitable service for this task because Glue is a fully managed, serverless ETL service that can natively connect to Amazon RDS MySQL via JDBC, apply transformations using PySpark, and write the output directly to S3 in Parquet format. This aligns perfectly with the requirement to extract, transform, and load a large dataset into a machine-learning-ready format without managing infrastructure.
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 ETL jobs with PySpark to read from RDS, apply transformations, and write to S3 as Parquet.
Why this is correct
AWS Glue ETL jobs with PySpark natively connect to Amazon RDS MySQL through JDBC, apply distributed transformations, and write Parquet directly to S3, satisfying the required format conversion. Glue's serverless Spark engine handles the large dataset scale without managing infrastructure, and its built-in SageMaker integration streamlines downstream machine learning consumption.
- ✗
Use Amazon Athena CTAS statements to copy data from RDS to S3.
Why it's wrong here
Athena queries data already in S3 or Glue Catalog; it cannot read directly from an RDS MySQL instance, so the extraction step fails. It is tempting because CTAS writes Parquet output, but that suits transforming existing S3 datasets, not pulling from a relational database.
- ✗
Use SageMaker Data Wrangler to connect to RDS and export transformed data to S3.
Why it's wrong here
Data Wrangler imports samples for interactive feature engineering within SageMaker, not bulk scheduled extraction from RDS at dataset scale. It is tempting because it exports to S3 in Parquet, but that fits exploratory preparation of modest samples, not production ETL pipelines.
- ✗
Use Amazon EMR with Spark to read from RDS, transform, and write to S3.
Why it's wrong here
Amazon EMR with Spark can read from RDS, transform data, and write Parquet to S3, but it introduces unnecessary cluster management overhead for a single ETL pipeline that could be executed serverlessly. This option is tempting because Spark is a powerful distributed processing engine ideal for large-scale, complex transformations across multiple data sources, and would be correct if the dataset required iterative processing or real-time streaming rather than a straightforward batch load into SageMaker.
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.