- A
Use columnar storage formats like Parquet or ORC
Columnar formats allow reading only required columns.
- B
Use LIMIT clause in SQL queries
Why wrong: LIMIT reduces returned rows, but Athena still scans all data.
- C
Convert data to CSV format
Why wrong: CSV is not compressed and increases data scanned compared to columnar formats.
- D
Create materialized views in Athena
Why wrong: Materialized views store results but do not reduce scan for the base data.
- E
Partition the data by a frequently filtered column
Partition pruning limits scans to relevant partitions.
Question 251 of 1,755
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 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 columnar storage formats like Parquet or ORC
A is correct because columnar storage formats like Parquet and ORC store data in a compressed, column-oriented layout. When Athena queries only a subset of columns, it can skip reading the entire row, drastically reducing the amount of data scanned from disk. This directly lowers the cost, as Athena charges based on the volume of data read per query.
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
The trap here is that candidates confuse the LIMIT clause with a query optimization technique, not realizing that Athena must still fully scan the underlying data to produce the limited result set, making it ineffective for cost reduction.
Detailed technical explanation
How to think about this question
Parquet and ORC use techniques like predicate pushdown and min/max statistics at the row group or stripe level to skip entire blocks of data that do not match filter conditions. For example, a query with a WHERE clause on a partitioned column can skip scanning partitions entirely if the partition pruning is enabled, but even within a partition, columnar formats allow skipping columns not referenced in the SELECT or WHERE clauses. In real-world scenarios, converting a 1 TB CSV dataset to Parquet with snappy compression can reduce scanned data by 70-90% for typical analytical queries.
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.
What to study next
Got this wrong? Here's your next step.
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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: Use columnar storage formats like Parquet or ORC — A is correct because columnar storage formats like Parquet and ORC store data in a compressed, column-oriented layout. When Athena queries only a subset of columns, it can skip reading the entire row, drastically reducing the amount of data scanned from disk. This directly lowers the cost, as Athena charges based on the volume of data read per query.
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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