← MLS-C01·Amazon Web Services

Question 392 of 1,755

Data Engineering →hardMultiple ChoiceObjective-mapped

Quick Answer

The correct answer is to use a coarser partition layout, such as partitioning only by date, and leverage Hive-style partitioning with AWS Glue Crawlers to avoid excessive small files. This is because partitioning by hour creates an extremely high number of small partitions, each with its own metadata overhead, which forces Athena to perform more file listing and metadata operations than actual data scanning. By consolidating partitions to a coarser granularity like day, you reduce the partition count, minimize metadata overhead, and allow Athena to efficiently prune large data scans even when querying a specific hour. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of how partition design directly impacts query performance and cost, often appearing as a trap where candidates mistakenly think more partitions always improve performance. A common memory tip is “coarser is faster for Athena”—think of partitions like chapters in a book; too many tiny chapters make finding a page slower than having fewer, thicker chapters.

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 engineer is setting up a data lake on Amazon S3 for a large retail company. The data includes customer transactions, inventory, and web logs. The company wants to use AWS Glue for ETL and Amazon Athena for ad-hoc queries. The data is partitioned by year, month, day, and hour. The engineer notices that Athena queries are slow and often scan large amounts of data even when only a specific hour is needed. The engineer has already enabled partitioning and used columnar formats like Parquet. What additional step should the engineer take to optimize query performance and reduce data scanned?

Question 1hardmultiple choice

Full question →

A
Use a coarser partition layout, such as partitioning only by date, and leverage Hive-style partitioning with AWS Glue Crawlers to avoid excessive small files.
Coarser partitions reduce the number of partitions and improve query planning.
B
Convert the Parquet files to CSV format to reduce the overhead of columnar storage and improve compression.
Why wrong: CSV is larger and slower to query than columnar formats like Parquet.
C
Use S3 Select to push down filters to S3, reducing the amount of data scanned by Athena.
Why wrong: S3 Select works at the object level, not across partitions, and Athena already push down filters.
D
Increase the granularity of partitioning to include minute-level partitions to further limit data scanned.
Why wrong: More partitions increase metadata overhead and can slow queries due to many small files.

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

✓

Use a coarser partition layout, such as partitioning only by date, and leverage Hive-style partitioning with AWS Glue Crawlers to avoid excessive small files.

Option C is correct because partitioning by hour alone can lead to many small files, which increases metadata overhead. Using a coarser partition like day and then using Hive-style partitioning with AWS Glue Crawlers will reduce the number of partitions and improve query performance. Option A is incorrect because S3 Select is for filtering within a single object, not for query optimization across multiple objects. Option B is incorrect because increasing the number of partitions further (e.g., adding minute) would worsen the small files problem. Option D is incorrect because converting to CSV would increase scan size and slow down queries.

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 a coarser partition layout, such as partitioning only by date, and leverage Hive-style partitioning with AWS Glue Crawlers to avoid excessive small files.
Why this is correct
Coarser partitions reduce the number of partitions and improve query planning.
Related concept
Read the scenario before looking for a memorised answer.
✗
Convert the Parquet files to CSV format to reduce the overhead of columnar storage and improve compression.
Why it's wrong here
CSV is larger and slower to query than columnar formats like Parquet.
✗
Use S3 Select to push down filters to S3, reducing the amount of data scanned by Athena.
Why it's wrong here
S3 Select works at the object level, not across partitions, and Athena already push down filters.
✗
Increase the granularity of partitioning to include minute-level partitions to further limit data scanned.
Why it's wrong here
More partitions increase metadata overhead and can slow queries due to many small files.

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.

Related MLS-C01 practice-question pages

Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.

Data Engineering practice questions

Practise MLS-C01 questions linked to Data Engineering.

Machine Learning Implementation and Operations practice questions

Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.

Modeling practice questions

Practise MLS-C01 questions linked to Modeling.

Exploratory Data Analysis practice questions

Practise MLS-C01 questions linked to Exploratory Data Analysis.

MLS-C01 fundamentals practice questions

Practise MLS-C01 questions linked to MLS-C01 fundamentals.

MLS-C01 scenario practice questions

Practise MLS-C01 questions linked to MLS-C01 scenario.

MLS-C01 troubleshooting practice questions

Practise MLS-C01 questions linked to MLS-C01 troubleshooting.

Practice this exam

Start a free MLS-C01 practice session

Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.

10 questions 20 questions 30 questions 50 questions Timed 30

MLS-C01 practice-test guide →Study guide →Browse all practice tests

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: Use a coarser partition layout, such as partitioning only by date, and leverage Hive-style partitioning with AWS Glue Crawlers to avoid excessive small files. — Option C is correct because partitioning by hour alone can lead to many small files, which increases metadata overhead. Using a coarser partition like day and then using Hive-style partitioning with AWS Glue Crawlers will reduce the number of partitions and improve query performance. Option A is incorrect because S3 Select is for filtering within a single object, not for query optimization across multiple objects. Option B is incorrect because increasing the number of partitions further (e.g., adding minute) would worsen the small files problem. Option D is incorrect because converting to CSV would increase scan size and slow down queries.

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 →

How Courseiva writes practice questions · Editorial policy

Same concept, more angles

1 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 scientist needs to query a 2 TB dataset stored in Amazon S3 using Amazon Athena. The data is in CSV format and is used for exploratory analysis. Queries are currently slow and expensive. Which action will improve query performance and reduce cost?

easy

A.Convert the data to JSON format to improve compression.
B.Increase the number of workers in the Athena query engine.
✓ C.Convert the data to Parquet format and partition by a commonly filtered column.
D.Create a composite index on the data using Athena's index feature.

Why C: Option D is correct because converting CSV to Parquet reduces scan size and query cost, and partitioning further limits data scanned. Option A is wrong because increasing workers is not applicable to Athena. Option B is wrong because converting to JSON may increase data size and cost. Option C is wrong because Athena does not use indexes.

Keep practising

Question Discussion

Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.

Loading comments…

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.