The answer is to use AWS Glue with Spark and dynamic frames, scaling the number of workers based on file size. This approach is the most cost-effective for large CSV to Redshift ETL because Glue’s Spark engine processes data in parallel across workers, and dynamic frames automatically infer schemas from large CSV files without manual parsing, reducing development overhead. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how to balance cost and scalability when handling large file ETL—a common trap is choosing Lambda (which has a 15-minute timeout and 10 GB memory limit, making it unsuitable for 10-50 GB files) or a single-node instance like r5.xlarge, which cannot handle peak loads. The key insight is that Glue’s worker scaling lets you pay only for the compute you need per job run, unlike EMR which incurs cluster overhead. Memory tip: “Glue scales, Lambda fails—big CSVs need Spark’s rails.”
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 engineering team is designing a data pipeline to process large CSV files (10-50 GB each) stored in Amazon S3. The pipeline must transform the data using AWS Glue and load it into Amazon Redshift for analytics. The team wants to minimize costs while ensuring the pipeline can handle peak loads. Which approach is the most cost-effective?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 with Spark and dynamic frames, scaling the number of workers based on file size.
Option D (use AWS Glue with Spark and dynamic frame) is correct because Glue's Spark-based ETL can handle large files efficiently, and using dynamic frames allows schema inference without manual parsing. Option A (use a single r5.xlarge) may not handle peak loads. Option B (Lambda) has time and memory limits unsuitable for large files. Option C (EMR with Hive) is more complex and typically more expensive than Glue for this use case.
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 Lambda to process each file and load into Redshift.
Why it's wrong here
Incorrect: Lambda has a 15-minute timeout and 10 GB memory limit, insufficient for multi-GB files.
✗
Use Amazon EMR with Hive to transform the data and load into Redshift.
Why it's wrong here
Incorrect: EMR is more expensive than Glue for sporadic jobs, and Hive adds overhead.
✗
Use an AWS Glue Python shell job with a single r5.xlarge worker.
Why it's wrong here
Incorrect: Python shell is not suitable for large files; it uses minimal resources.
✓
Use AWS Glue with Spark and dynamic frames, scaling the number of workers based on file size.
Why this is correct
Correct: Glue Spark jobs handle large files efficiently; dynamic frames simplify schema handling.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
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 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 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: Use AWS Glue with Spark and dynamic frames, scaling the number of workers based on file size. — Option D (use AWS Glue with Spark and dynamic frame) is correct because Glue's Spark-based ETL can handle large files efficiently, and using dynamic frames allows schema inference without manual parsing. Option A (use a single r5.xlarge) may not handle peak loads. Option B (Lambda) has time and memory limits unsuitable for large files. Option C (EMR with Hive) is more complex and typically more expensive than Glue for this use case.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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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