- A
Amazon Redshift
Why wrong: Redshift is a data warehouse, not an ETL engine.
- B
Amazon EMR
EMR provides a managed Spark environment with Hive support and allows custom PySpark code.
- C
AWS Glue
Why wrong: Glue supports PySpark but has limitations on custom libraries and configurations.
- D
Amazon SageMaker
Why wrong: SageMaker is for ML training and inference, not general-purpose ETL.
Question 401 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 data scientist needs to run complex ETL transformations on a large dataset stored in Amazon S3. The transformations are written in PySpark and require occasional access to Hive metastore. The solution should minimize operational overhead and allow the data scientist to focus on code development. Which AWS service should be used?
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 natively supports PySpark and Hive metastore integration, allowing the data scientist to run complex ETL transformations on large datasets stored in S3 with minimal operational overhead. EMR provides managed clusters that automatically scale and handle infrastructure, enabling the data scientist to focus on code development rather than cluster management.
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 Redshift
Why it's wrong here
Redshift is a data warehouse, not an ETL engine.
- ✓
Amazon EMR
Why this is correct
EMR provides a managed Spark environment with Hive support and allows custom PySpark code.
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.
- ✗
AWS Glue
Why it's wrong here
Glue supports PySpark but has limitations on custom libraries and configurations.
- ✗
Amazon SageMaker
Why it's wrong here
SageMaker is for ML training and inference, not general-purpose ETL.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse AWS Glue's serverless Spark environment with the ability to run arbitrary PySpark code with Hive metastore access, but Glue abstracts away cluster management and does not provide the same level of control or direct Hive metastore integration as EMR.
Detailed technical explanation
How to think about this question
Amazon EMR uses Apache Spark and Hive as core components, allowing direct integration with the Hive metastore via the Hive Metastore Service (HMS) or external metastores like AWS Glue Data Catalog. Under the hood, EMR leverages YARN for resource management and can read/write data directly from S3 using the EMR File System (EMRFS), which provides strong consistency and performance optimizations like S3 Select and S3A committers. A real-world scenario where this matters is when a data scientist needs to run iterative PySpark transformations that join large datasets with Hive table metadata, requiring low-latency metastore access that EMR's managed clusters provide without the overhead of provisioning servers.
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.
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: Amazon EMR — Amazon EMR is the correct choice because it natively supports PySpark and Hive metastore integration, allowing the data scientist to run complex ETL transformations on large datasets stored in S3 with minimal operational overhead. EMR provides managed clusters that automatically scale and handle infrastructure, enabling the data scientist to focus on code development rather than cluster management.
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
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