The answer is AWS Glue ETL jobs with PySpark. This is the correct choice because Glue is a fully managed, serverless ETL service that natively connects to Amazon RDS MySQL via JDBC, allowing you to extract large datasets, apply transformations using PySpark, and write the output directly to S3 in Parquet format—a columnar storage format ideal for machine learning workloads in SageMaker. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of the optimal ETL pipeline for preparing structured data for ML, often appearing as a trap where candidates might mistakenly choose AWS Data Pipeline or Amazon Athena for transformations. The key distinction is that Glue provides built-in PySpark for complex transforms and automatic schema discovery, while Athena is primarily for querying data in place. Remember the mnemonic: Glue Gets Parquet—for RDS to S3, Glue is the glue that holds your ETL together.
MLA-C01 Data Preparation for Machine Learning 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. 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 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?
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.
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.
✓
Use AWS Glue ETL jobs with PySpark to read from RDS, apply transformations, and write to S3 as Parquet.
Why this is correct
Glue is purpose-built for this workload.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Amazon Athena CTAS statements to copy data from RDS to S3.
Why it's wrong here
Athena queries data in S3; it cannot read from RDS directly.
✗
Use SageMaker Data Wrangler to connect to RDS and export transformed data to S3.
Why it's wrong here
Data Wrangler cannot connect to external databases; it requires data to already be in S3.
✗
Use Amazon EMR with Spark to read from RDS, transform, and write to S3.
Why it's wrong here
EMR is viable but requires cluster management, making Glue simpler.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
Under the hood, AWS Glue uses a Spark-based runtime that reads from RDS via JDBC connections, automatically handling parallelism by partitioning the table based on a numeric column or using a custom query. When writing to S3 in Parquet format, Glue can leverage dynamic frame transformations to handle schema evolution and compression (e.g., Snappy), which is critical for large datasets to reduce storage costs and improve query performance in SageMaker. A real-world scenario where this matters is when dealing with multi-terabyte tables where manual partitioning and column pruning in Glue can significantly reduce data shuffling and job execution time compared to a generic Spark cluster.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLA-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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Question Discussion
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