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Data Preparation for Machine LearningeasyMultiple ChoiceObjective-mapped

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.

An ML engineer needs to convert a raw dataset from CSV to Parquet format in a serverless manner for cost efficiency. Which AWS service can be used to perform this conversion without managing servers?

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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

AWS Glue is correct because it provides a serverless ETL service that can automatically convert CSV to Parquet using its built-in transform capabilities, such as the `ChangeSchema` or `ConvertToParquet` transforms in a Glue ETL job. This eliminates the need to provision or manage any servers, aligning with the cost-efficiency requirement.

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.

  • Amazon S3 Select

    Why it's wrong here

    S3 Select retrieves subsets of data, does not convert format.

  • Amazon EMR

    Why it's wrong here

    EMR requires provisioning and managing clusters.

  • AWS Lambda

    Why it's wrong here

    Lambda is not designed for large-scale ETL; time and memory limits apply.

  • AWS Glue

    Why this is correct

    Glue provides serverless Spark jobs for format conversion.

    Related concept

    Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse AWS Glue's serverless ETL capability with Amazon EMR's managed clusters, assuming EMR is also serverless, but EMR requires explicit cluster management and is not truly serverless like Glue.

Detailed technical explanation

How to think about this question

Under the hood, AWS Glue uses Apache Spark as its execution engine, and the Parquet conversion leverages Spark's optimized columnar storage format, which reduces I/O and improves compression compared to row-based CSV. A subtle behavior is that Glue's DynamicFrame automatically infers schema from CSV, but you must handle data types carefully (e.g., timestamps) to avoid schema mismatch errors during conversion. In real-world scenarios, this is critical for ML pipelines where Parquet's predicate pushdown can significantly speed up subsequent data loading for training.

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.

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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: AWS Glue — AWS Glue is correct because it provides a serverless ETL service that can automatically convert CSV to Parquet using its built-in transform capabilities, such as the `ChangeSchema` or `ConvertToParquet` transforms in a Glue ETL job. This eliminates the need to provision or manage any servers, aligning with the cost-efficiency requirement.

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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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.