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Data Engineering →hardMultiple ChoiceObjective-mapped

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.

A
Use AWS Lambda to process each file and load into Redshift.
Why wrong: Incorrect: Lambda has a 15-minute timeout and 10 GB memory limit, insufficient for multi-GB files.
B
Use Amazon EMR with Hive to transform the data and load into Redshift.
Why wrong: Incorrect: EMR is more expensive than Glue for sporadic jobs, and Hive adds overhead.
C
Use an AWS Glue Python shell job with a single r5.xlarge worker.
Why wrong: Incorrect: Python shell is not suitable for large files; it uses minimal resources.
D
Use AWS Glue with Spark and dynamic frames, scaling the number of workers based on file size.
Correct: Glue Spark jobs handle large files efficiently; dynamic frames simplify schema handling.

Full breakdown with real-world context →

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 with Spark and dynamic frames, scaling the number of workers based on file size.

AWS Glue with Spark and dynamic frames is the most cost-effective approach because it is serverless, automatically scales workers based on file size, and is optimized for ETL on large CSV files (10-50 GB) in S3. Dynamic frames provide built-in transformations and schema inference, reducing development effort, while the ability to adjust the number of workers allows handling peak loads without over-provisioning. This minimizes idle compute costs compared to always-on clusters like EMR.

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

The trap here is that candidates often choose AWS Lambda (Option A) for its low cost and simplicity, failing to recognize its strict execution limits (15-minute timeout, 10 GB memory) that make it impractical for multi-GB file processing, or they pick EMR (Option B) assuming it is always cheaper, ignoring the overhead of cluster management and idle costs.

Detailed technical explanation

How to think about this question

AWS Glue with Spark uses Apache Spark under the hood, which partitions large CSV files across multiple workers for parallel processing, and dynamic frames automatically handle schema evolution and data types, reducing manual coding. The number of workers can be set dynamically using the 'MaxCapacity' or 'NumberOfWorkers' parameter, and Glue's auto-scaling feature adjusts resources during job execution based on shuffle operations and data volume. In a real-world scenario, a 50 GB CSV file might be split into hundreds of partitions, each processed by a separate executor, enabling completion within minutes while keeping costs proportional to actual usage.

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.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

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 MLS-C01 question test?

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. — AWS Glue with Spark and dynamic frames is the most cost-effective approach because it is serverless, automatically scales workers based on file size, and is optimized for ETL on large CSV files (10-50 GB) in S3. Dynamic frames provide built-in transformations and schema inference, reducing development effort, while the ability to adjust the number of workers allows handling peak loads without over-provisioning. This minimizes idle compute costs compared to always-on clusters like EMR.

What should I do if I get this MLS-C01 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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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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.