- A
Use a SageMaker Processing job with a custom Python script that reads from both sources and writes to S3.
Why wrong: Processing jobs require manual resource management and are not serverless.
- B
Use Amazon Athena to join the data from RDS and S3, then export the results as Parquet.
Why wrong: Athena is not designed for complex ETL transformations like text vectorization.
- C
Use AWS Glue ETL with a Spark script that reads from RDS (via JDBC) and S3, performs transformations, and writes Parquet.
Glue provides a serverless Spark environment capable of handling both sources and complex transformations.
- D
Use Amazon Kinesis Data Analytics to read from RDS and S3 and produce a continuous stream of processed data.
Why wrong: Kinesis Data Analytics is for streaming, not batch ETL of large datasets.
Quick Answer
The correct approach is to use AWS Glue ETL with a Spark script that reads from RDS via JDBC and S3, performs transformations, and writes Parquet. This works because Glue natively handles both structured data from relational databases and unstructured JSON files from S3 within a single serverless Spark job, allowing you to apply complex feature engineering like text vectorization directly in the transformation step. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of how to architect a unified, cost-effective batch pipeline for ML training data—a common trap is reaching for Amazon SageMaker Processing or Athena, but those lack the native JDBC and complex transformation capabilities of Glue ETL. Remember the memory tip: “Glue sticks structured and unstructured together” for a single Parquet output.
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 is building a data pipeline for a machine learning model that requires both structured and unstructured data. The structured data (customer demographics) is in Amazon RDS, and the unstructured data (customer support chat logs) is in Amazon S3 as JSON files. The engineer needs to combine these datasets into a single training dataset stored in S3 in Parquet format. They must also perform feature engineering such as text vectorization on the chat logs. The pipeline should be serverless and cost-effective. Which approach should they use?
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 a Spark script that reads from RDS (via JDBC) and S3, performs transformations, and writes Parquet.
AWS Glue ETL with a Spark script is the correct choice because it natively supports reading from both Amazon RDS (via JDBC) and Amazon S3 (JSON), performing complex transformations like text vectorization, and writing the output as Parquet. Glue is serverless, cost-effective (pay per DPU-hour), and fully managed, making it ideal for batch ETL pipelines that combine structured and unstructured data for ML training.
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 a SageMaker Processing job with a custom Python script that reads from both sources and writes to S3.
Why it's wrong here
Processing jobs require manual resource management and are not serverless.
- ✗
Use Amazon Athena to join the data from RDS and S3, then export the results as Parquet.
Why it's wrong here
Athena is not designed for complex ETL transformations like text vectorization.
- ✓
Use AWS Glue ETL with a Spark script that reads from RDS (via JDBC) and S3, performs transformations, and writes Parquet.
Why this is correct
Glue provides a serverless Spark environment capable of handling both sources and complex transformations.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Amazon Kinesis Data Analytics to read from RDS and S3 and produce a continuous stream of processed data.
Why it's wrong here
Kinesis Data Analytics is for streaming, not batch ETL of large datasets.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose SageMaker Processing (Option A) because it is associated with ML, but they overlook that Glue ETL is the designated AWS service for serverless data preparation and transformation, especially when combining disparate data sources like RDS and S3.
Detailed technical explanation
How to think about this question
Under the hood, AWS Glue ETL uses Apache Spark with dynamic frames that automatically infer schemas from JSON and JDBC sources. The text vectorization step can be implemented using Spark MLlib's Tokenizer or Word2Vec within the same script, avoiding data movement. Glue's serverless architecture scales DPU capacity based on data volume, and its pushdown predicates can filter RDS data at the source via JDBC, reducing network transfer costs.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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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 — 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 with a Spark script that reads from RDS (via JDBC) and S3, performs transformations, and writes Parquet. — AWS Glue ETL with a Spark script is the correct choice because it natively supports reading from both Amazon RDS (via JDBC) and Amazon S3 (JSON), performing complex transformations like text vectorization, and writing the output as Parquet. Glue is serverless, cost-effective (pay per DPU-hour), and fully managed, making it ideal for batch ETL pipelines that combine structured and unstructured data for ML training.
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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Last reviewed: Jun 24, 2026
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