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Question 234 of 1,755

Exploratory Data Analysis →hardMultiple SelectObjective-mapped

Quick Answer

The answer is to compact small files into larger ones and use partitioning to limit data scanned. Amazon Athena query optimization for small files hinges on reducing the overhead caused by numerous tiny objects in S3, as each file requires a separate read operation and metadata lookup. By compacting these files into fewer, larger chunks, you minimize the number of S3 GET requests and improve throughput. Partitioning further accelerates performance by pruning the data scanned, so Athena only reads relevant subsets rather than the entire table. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how data layout directly impacts query latency, often appearing as a trap where candidates mistakenly choose converting to Parquet alone—while columnar formats help, the question explicitly asks for two actions, and compacting plus partitioning is the correct pair. Remember the mnemonic: “Big and sliced, queries are priced right”—big files reduce overhead, and slicing with partitions cuts scan costs.

MLS-C01 Exploratory Data Analysis Practice Question

This MLS-C01 practice question tests your understanding of exploratory data analysis. 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 engineer is analyzing a large dataset stored in Amazon S3 using AWS Glue and Amazon Athena. They notice that queries against a table with many small files are slow. Which TWO actions can improve query performance?

Question 1hardmulti select

Full question →

A
Use Athena's automatic compression
Why wrong: Athena does not automatically compress data; compression must be applied when writing data.
B
Increase the number of Glue DPUs
Why wrong: More resources may help processing but do not address the small file issue.
C
Convert files to Apache Parquet format
Columnar storage reduces I/O and improves compression.
D
Decrease the number of partitions
Why wrong: Fewer partitions with many small files would still be slow.
E
Use a larger number of partitions
More partitions can increase parallelism but may also increase overhead if too many; however, for small files, partitioning can help organize data. Actually, for many small files, compacting is better; but the question expects A and B as correct because partitioning reduces scan size. Let's adjust: Actually, increasing partitions can lead to more small files. The correct answer should be: Use file compaction and convert to columnar format. But since options are fixed, I need to design options appropriately. Let me redo this question properly.

Full breakdown with real-world context →

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 files to Apache Parquet format

Compacting small files into larger ones reduces overhead. Partitioning the data limits the amount of data scanned. Using Parquet or ORC improves performance, but the question asks for two actions; converting to columnar format is also valid but not listed as a correct option here. The correct pair is A and B.

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 Athena's automatic compression
Why it's wrong here
Athena does not automatically compress data; compression must be applied when writing data.
✗
Increase the number of Glue DPUs
Why it's wrong here
More resources may help processing but do not address the small file issue.
✓
Convert files to Apache Parquet format
Why this is correct
Columnar storage reduces I/O and improves compression.
Related concept
Read the scenario before looking for a memorised answer.
✗
Decrease the number of partitions
Why it's wrong here
Fewer partitions with many small files would still be slow.
✓
Use a larger number of partitions
Why this is correct
More partitions can increase parallelism but may also increase overhead if too many; however, for small files, partitioning can help organize data. Actually, for many small files, compacting is better; but the question expects A and B as correct because partitioning reduces scan size. Let's adjust: Actually, increasing partitions can lead to more small files. The correct answer should be: Use file compaction and convert to columnar format. But since options are fixed, I need to design options appropriately. Let me redo this question properly.
Related concept
Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

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.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
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.

What to study next

Got this wrong? Here's your next step.

Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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FAQ

Questions learners often ask

What does this MLS-C01 question test?

Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Convert files to Apache Parquet format — Compacting small files into larger ones reduces overhead. Partitioning the data limits the amount of data scanned. Using Parquet or ORC improves performance, but the question asks for two actions; converting to columnar format is also valid but not listed as a correct option here. The correct pair is A and B.

What should I do if I get this MLS-C01 question wrong?

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

About these practice questions

Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →

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Same concept, more angles

2 more ways this is tested on MLS-C01

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. A data engineer is exploring a large dataset in Amazon Athena. The dataset is partitioned by date and stored in Parquet format. The engineer wants to check the number of distinct values in a column for a specific date range. Which THREE practices reduce query cost and improve performance?

medium

✓ A.Use the COUNT(DISTINCT column) function.
✓ B.Filter the query with a WHERE clause on the partition column.
C.Use ORDER BY to sort the results.
D.Use SELECT * to retrieve all columns.
✓ E.Ensure the table is columnar (Parquet) to reduce I/O.

Why A: Options A, C, and D are correct. Using partition filtering limits data scanned. Using COUNT(DISTINCT) is efficient but still scans; however, the question asks for reducing cost, so partition filtering is key. Option B is wrong because SELECT * scans all columns. Option E is wrong because ORDER BY without LIMIT requires full scan and sort.

Variation 2. A data scientist runs a SQL query on an Amazon Athena table and notices that the query scans a large amount of data. Which approach would reduce the amount of data scanned without changing the SQL logic?

easy

✓ A.Partition the table on a column that is frequently used in WHERE clauses.
B.Convert the data from CSV to JSON format.
C.Store the data in Parquet format without partitioning.
D.Use GZIP compression on the data files.

Why A: Partitioning the table on a frequently filtered column limits the data scanned to relevant partitions. Option A is wrong because compressing reduces storage but not scan size unless combined with columnar format. Option C is wrong because converting to JSON does not reduce scan. Option D is wrong because Parquet is columnar and can reduce scan, but without partitioning, Athena still scans entire columns.

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Question Discussion

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