This MLS-C01 practice question tests your understanding of data engineering. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 runs the above CLI command and sees that the bucket contains many small Parquet files (1 MB each) under the prefix. When querying this data with Athena, the query performance is poor and costs are high. Which approach would MOST improve performance and reduce cost?
Convert the files to JSON format
Why wrong: JSON is also not columnar and can be larger.
Convert the files to CSV format
Why wrong: CSV is not columnar and may increase scan size.
Consolidate the small files into fewer, larger Parquet files
Fewer, larger files reduce overhead and improve compression.
Add more partitions by including hour in the prefix
Why wrong: More partitions could lead to even more small files.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Consolidate the small files into fewer, larger Parquet files
C is correct because consolidating many small Parquet files into fewer, larger files (e.g., 128–256 MB each) reduces the overhead of Amazon Athena's file listing and metadata operations, and improves compression and predicate pushdown efficiency. Parquet is a columnar format optimized for analytics, so keeping it while reducing file count directly addresses the root cause of poor performance and high cost.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Convert the files to JSON format
Why it's wrong here
JSON is also not columnar and can be larger.
Convert the files to CSV format
Why it's wrong here
CSV is not columnar and may increase scan size.
Consolidate the small files into fewer, larger Parquet files
Why this is correct
Fewer, larger files reduce overhead and improve compression.
Related concept
Read the scenario before looking for a memorised answer.
Add more partitions by including hour in the prefix
Why it's wrong here
More partitions could lead to even more small files.
Common exam traps
The trap here is that candidates may think adding more partitions always improves query performance, but in this scenario with many tiny files, more partitions would exacerbate the small-file problem and increase Athena's overhead.
Detailed technical explanation
Athena charges based on the amount of data scanned per query, and small Parquet files cause excessive file listing overhead (each file requires a separate S3 GET request and metadata lookup) and reduce the effectiveness of Parquet's row group pruning. Consolidating files to a target size of 128–256 MB aligns with Athena's optimal split size (default 128 MB) and allows better parallelization across workers, reducing both scan time and cost.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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FAQ
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
The correct answer is: Consolidate the small files into fewer, larger Parquet files — C is correct because consolidating many small Parquet files into fewer, larger files (e.g., 128–256 MB each) reduces the overhead of Amazon Athena's file listing and metadata operations, and improves compression and predicate pushdown efficiency. Parquet is a columnar format optimized for analytics, so keeping it while reducing file count directly addresses the root cause of poor performance and high cost.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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