The answer is AWS Glue, because it provides a fully serverless Spark environment ideal for transforming large CSV files into Parquet format without managing any infrastructure. AWS Glue ETL for CSV to Parquet handles complex custom Python logic natively through its Spark runtime, automatically scaling to process massive datasets stored in Amazon S3 while minimizing operational overhead. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of serverless ETL versus compute-bound services; a common trap is choosing AWS Lambda, which has a 15-minute timeout and cannot handle large file conversions, or Amazon EMR, which requires cluster management. Remember that Glue is purpose-built for batch ETL with custom code, while Lambda is for lightweight, short-lived functions. A useful memory tip: “Glue sticks big data together serverlessly, Lambda only handles quick jobs.”
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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 transform large CSV files stored in Amazon S3 into Parquet format before loading into Amazon Redshift. The transformation logic is complex and requires custom Python code. Which AWS service should be used to perform this transformation with minimal operational overhead?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
AWS Glue
Option B is correct because AWS Glue provides a serverless Spark environment with built-in support for ETL jobs, including converting CSV to Parquet. Option A (AWS Lambda) has a 15-minute timeout and is not suitable for large files. Option C (Amazon EMR) requires managing clusters. Option D (AWS Data Pipeline) is a legacy service with less flexibility.
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.
✓
AWS Glue
Why this is correct
Glue is a serverless ETL service that can run complex transformations on data in S3 and write to Parquet.
Related concept
Read the scenario before looking for a memorised answer.
✗
AWS Lambda
Why it's wrong here
Lambda has execution time limits and is not designed for heavy ETL transformations on large datasets.
✗
Amazon EMR
Why it's wrong here
EMR requires manual cluster management and is more operationally heavy than needed.
✗
AWS Data Pipeline
Why it's wrong here
Data Pipeline is a legacy service with less flexibility and higher maintenance compared to Glue.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: AWS Glue — Option B is correct because AWS Glue provides a serverless Spark environment with built-in support for ETL jobs, including converting CSV to Parquet. Option A (AWS Lambda) has a 15-minute timeout and is not suitable for large files. Option C (Amazon EMR) requires managing clusters. Option D (AWS Data Pipeline) is a legacy service with less flexibility.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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This MLS-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 MLS-C01 exam.
Question Discussion
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