The answer is AWS Glue, which should be used to create a crawler and ETL job that converts CSV to Parquet. This is correct because AWS Glue is a serverless, pay-per-use ETL service that can automatically transform large datasets without writing custom code; the crawler infers the schema from the CSV files in S3, and the built-in Spark-based ETL job efficiently converts the data to the columnar Parquet format, which significantly improves query and training performance for SageMaker by reducing I/O and storage costs. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of cost-effective data preparation for ML pipelines—a common trap is choosing Amazon EMR or Athena, but Glue is optimal here because it handles 10 TB with minimal overhead and no cluster management. Remember the memory tip: “Glue sticks CSV to Parquet for cheap ML prep.”
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 (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?
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.
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 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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
AWS Glue uses Apache Spark under the hood for its ETL jobs, enabling distributed processing of large datasets like 10 TB. The conversion from CSV to Parquet leverages columnar storage and predicate pushdown, which reduces I/O and improves query performance in SageMaker training. A real-world scenario where this matters is when you need to process petabyte-scale datasets with minimal cost, as Glue's dynamic scaling and spot instance support can significantly reduce expenses compared to always-on EMR clusters.
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.
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 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.
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.
About these practice questions
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Variation 1. An organization stores raw data in Amazon S3 as CSV files. They need to perform serverless data transformation and convert the data to Parquet format for efficient ML training. Which AWS service is most appropriate?
easy
✓ A.AWS Glue
B.Amazon EMR
C.Amazon Athena
D.Amazon Redshift
Why A: AWS Glue is the most appropriate service because it is a fully managed, serverless ETL service designed specifically for data transformation tasks like converting CSV to Parquet. It automatically handles schema inference, data partitioning, and optimization for ML training workloads without requiring infrastructure management.
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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.
Question Discussion
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