- A
Use Amazon Athena to transform the data.
Why wrong: Athena is for querying, not ETL transformations.
- B
Increase the number of DPUs to 100.
Why wrong: More DPUs may improve speed but increase cost proportionally.
- C
Use Amazon EMR with Spot Instances instead of AWS Glue.
Why wrong: EMR can be cheaper but requires more management.
- D
Convert the CSV files to Parquet format and partition the data by a column.
Parquet reduces I/O and partitioning reduces data scanned.
Question 104 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 machine learning team is preparing a large dataset for training. The dataset consists of 10,000 CSV files, each about 100 MB, stored in Amazon S3. The team wants to transform the data using AWS Glue ETL jobs. The transformation involves filtering rows, adding new columns, and joining with a small reference table (100 KB). The team is concerned about job performance and cost. They currently have a Glue job with 10 DPU (Data Processing Units) and it takes about 2 hours to complete. The team wants to reduce the runtime and cost. Which approach should they take?
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
✓
Convert the CSV files to Parquet format and partition the data by a column.
Converting the CSV files to Parquet format and partitioning the data by a column significantly reduces the amount of data scanned and processed by AWS Glue. Parquet is a columnar storage format that allows Glue to read only the necessary columns, and partitioning enables predicate pushdown to skip irrelevant partitions. This directly reduces I/O and compute requirements, leading to faster job runtime and lower cost without increasing DPU count.
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 Amazon Athena to transform the data.
Why it's wrong here
Athena is for querying, not ETL transformations.
- ✗
Increase the number of DPUs to 100.
Why it's wrong here
More DPUs may improve speed but increase cost proportionally.
- ✗
Use Amazon EMR with Spot Instances instead of AWS Glue.
Why it's wrong here
EMR can be cheaper but requires more management.
- ✓
Convert the CSV files to Parquet format and partition the data by a column.
Why this is correct
Parquet reduces I/O and partitioning reduces data scanned.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that simply adding more compute resources (DPUs) will linearly improve performance, ignoring the critical impact of data format and partitioning on I/O and shuffle efficiency.
Detailed technical explanation
How to think about this question
Parquet uses columnar compression and encoding (e.g., dictionary encoding, run-length encoding) that can reduce storage size by 70-90% compared to CSV, and AWS Glue's Spark engine can leverage predicate pushdown and column pruning to read only the data needed for transformations. Partitioning by a high-cardinality column (e.g., date or region) allows Glue to skip entire S3 prefixes during job execution, dramatically reducing the amount of data shuffled and processed. In practice, a well-partitioned Parquet dataset can reduce Glue job runtime from hours to minutes while cutting costs by over 50%.
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
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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: Convert the CSV files to Parquet format and partition the data by a column. — Converting the CSV files to Parquet format and partitioning the data by a column significantly reduces the amount of data scanned and processed by AWS Glue. Parquet is a columnar storage format that allows Glue to read only the necessary columns, and partitioning enables predicate pushdown to skip irrelevant partitions. This directly reduces I/O and compute requirements, leading to faster job runtime and lower cost without increasing DPU count.
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
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