- A
Amazon EMR
EMR provides managed Spark clusters for complex transformations.
- B
Amazon EC2 with manually configured Spark
Why wrong: Increases operational overhead.
- C
Amazon Athena
Why wrong: Athena is for SQL queries, not Spark.
- D
AWS Glue
Why wrong: Glue is best for simple ETL, not complex Spark jobs.
Question 68 of 1,755
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 company needs to perform complex transformations on large datasets stored in Amazon S3 using Apache Spark. They want to minimize operational overhead. Which AWS service should they use?
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 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
✓
Amazon EMR
Amazon EMR is the correct choice because it is a managed big data platform that natively runs Apache Spark, allowing you to perform complex transformations on large datasets stored in Amazon S3 without provisioning or managing underlying infrastructure. EMR automatically handles cluster lifecycle, scaling, and tuning, minimizing operational overhead while providing full Spark compatibility.
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 EMR
Why this is correct
EMR provides managed Spark clusters for complex transformations.
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.
- ✗
Amazon EC2 with manually configured Spark
Why it's wrong here
Increases operational overhead.
- ✗
Amazon Athena
Why it's wrong here
Athena is for SQL queries, not Spark.
- ✗
AWS Glue
Why it's wrong here
Glue is best for simple ETL, not complex Spark jobs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse AWS Glue's Spark-based ETL engine with a full-fledged Spark cluster, overlooking that Glue is optimized for simpler, serverless ETL jobs and lacks the fine-grained control and performance tuning capabilities of Amazon EMR for complex transformations.
Detailed technical explanation
How to think about this question
Amazon EMR uses a managed Hadoop cluster with Spark as a first-class citizen, supporting features like dynamic scaling, spot instance integration, and optimized I/O with S3 via the EMRFS connector. Under the hood, EMR can leverage the S3A filesystem and the S3 Select pushdown predicate to reduce data scanned, and it supports advanced Spark tuning parameters (e.g., spark.sql.shuffle.partitions, spark.executor.memory) that are critical for complex transformations on petabyte-scale datasets. In real-world scenarios, EMR is often chosen for iterative machine learning pipelines where you need to cache intermediate DataFrames in memory across stages, something that Glue's serverless environment handles less efficiently due to its auto-scaling and ephemeral nature.
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 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.
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.
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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: Amazon EMR — Amazon EMR is the correct choice because it is a managed big data platform that natively runs Apache Spark, allowing you to perform complex transformations on large datasets stored in Amazon S3 without provisioning or managing underlying infrastructure. EMR automatically handles cluster lifecycle, scaling, and tuning, minimizing operational overhead while providing full Spark compatibility.
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.
About these practice questions
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Last reviewed: Jul 4, 2026
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.
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