MLS-C01 Data Engineering Practice Question
Which TWO options are valid ways to reduce the amount of data scanned by Amazon Athena queries, thereby reducing cost?
⚠ Common exam trap
It's easy for candidates to 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.
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.
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.
- ✗
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.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.