The answer is partitioning data by a frequently filtered column and using columnar storage formats like Parquet. Partitioning works by physically separating data into folders based on a key, such as date or region, so Athena can skip entire partitions that don’t match the query’s WHERE clause, drastically reducing the data scanned. Columnar formats like Parquet or ORC further minimize costs because they read only the specific columns needed for a query, rather than entire rows, which is far more efficient than row-based formats like CSV. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of cost optimization in data lake analytics, often appearing in scenario-based questions where you must choose between partitioning, compression, or format changes. A common trap is assuming that limiting the number of rows returned reduces scan cost—it does not, as Athena still reads all underlying data. Remember the mnemonic “Partition and Parquet slash costs” to link the two key strategies.
MLS-C01 Data Engineering Practice Question
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.
Which TWO options are valid ways to reduce the amount of data scanned by Amazon Athena queries, thereby reducing cost?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use columnar storage formats like Parquet or ORC
Partitioning allows Athena to skip entire partitions. Using columnar formats like Parquet reduces the amount of data read per column. Converting to CSV increases data scanned. Materialized views don't reduce scan. Limiting row count does not reduce scan of underlying data.
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 columnar storage formats like Parquet or ORC
Why this is correct
Columnar formats allow reading only required columns.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use LIMIT clause in SQL queries
Why it's wrong here
LIMIT reduces returned rows, but Athena still scans all data.
✗
Convert data to CSV format
Why it's wrong here
CSV is not compressed and increases data scanned compared to columnar formats.
✗
Create materialized views in Athena
Why it's wrong here
Materialized views store results but do not reduce scan for the base data.
✓
Partition the data by a frequently filtered column
Why this is correct
Partition pruning limits scans to relevant partitions.
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 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.
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.
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 columnar storage formats like Parquet or ORC — Partitioning allows Athena to skip entire partitions. Using columnar formats like Parquet reduces the amount of data read per column. Converting to CSV increases data scanned. Materialized views don't reduce scan. Limiting row count does not reduce scan of underlying data.
What should I do if I get this MLS-C01 question wrong?
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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