- A
Store data in columnar formats like Parquet or ORC.
Columnar formats improve query performance and reduce scan costs for Athena and Redshift Spectrum.
- B
Use the AWS Glue Data Catalog as a central metadata repository.
Athena, Redshift Spectrum, and EMR all integrate with the Glue Data Catalog.
- C
Enable S3 Select on the target buckets.
Why wrong: S3 Select is a feature for filtering data, not a configuration required for querying.
- D
Enable S3 versioning on all buckets.
Why wrong: Versioning is for data protection, not necessary for querying.
- E
Set up Kinesis Data Firehose for streaming ingestion.
Why wrong: Streaming ingestion is optional; the question asks for essential configurations.
Question 351 of 1,755
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 set up a data lake on S3 that supports both batch and streaming ingestion. The data must be queryable by Athena, Redshift Spectrum, and EMR. Which TWO configurations are essential? (Choose two.)
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
✓
Store data in columnar formats like Parquet or ORC.
Option A is correct because columnar formats like Parquet and ORC are optimized for analytical queries, reducing I/O by reading only the necessary columns. This is essential for Athena, Redshift Spectrum, and EMR, which all benefit from the efficient compression and predicate pushdown capabilities of these formats, enabling faster query performance and lower costs.
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.
- ✓
Store data in columnar formats like Parquet or ORC.
Why this is correct
Columnar formats improve query performance and reduce scan costs for Athena and Redshift Spectrum.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use the AWS Glue Data Catalog as a central metadata repository.
Why this is correct
Athena, Redshift Spectrum, and EMR all integrate with the Glue Data Catalog.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable S3 Select on the target buckets.
Why it's wrong here
S3 Select is a feature for filtering data, not a configuration required for querying.
- ✗
Enable S3 versioning on all buckets.
Why it's wrong here
Versioning is for data protection, not necessary for querying.
- ✗
Set up Kinesis Data Firehose for streaming ingestion.
Why it's wrong here
Streaming ingestion is optional; the question asks for essential configurations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse the ingestion mechanism (e.g., Kinesis Data Firehose) with the essential data lake configuration, or assume that S3 Select is required for queryability, when in fact the core requirements are a unified metadata catalog and an efficient storage format.
Detailed technical explanation
How to think about this question
Under the hood, columnar formats like Parquet store data by column rather than by row, enabling compression algorithms like dictionary encoding and run-length encoding to achieve up to 75% storage reduction. In real-world scenarios, using Parquet with partitioning on date or region columns allows Athena to prune partitions and scan only relevant data, drastically reducing query costs and latency.
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.
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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: Store data in columnar formats like Parquet or ORC. — Option A is correct because columnar formats like Parquet and ORC are optimized for analytical queries, reducing I/O by reading only the necessary columns. This is essential for Athena, Redshift Spectrum, and EMR, which all benefit from the efficient compression and predicate pushdown capabilities of these formats, enabling faster query performance and lower costs.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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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