Quick Answer
The answer is Parquet and ORC. These two data formats are columnar and optimized for analytics queries in Amazon S3 because they store data by column rather than by row, which allows query engines like Amazon Athena and Redshift Spectrum to read only the specific columns needed for a given aggregation or filter, dramatically reducing I/O and scan costs. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this distinction tests your understanding of efficient data storage for large-scale ML pipelines, where columnar formats like Parquet and ORC are preferred over row-oriented formats like JSON, CSV, or Avro. A common trap is assuming Avro is columnar—it is actually row-oriented and better suited for streaming or write-heavy workloads. To remember: think of columnar formats as “vertical” storage for analytical “vertical” queries, while row formats are “horizontal” for transactional reads.
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 data formats are columnar and optimized for analytics queries in Amazon S3?
CSV
Why wrong: CSV is row-oriented.
ORC
ORC is columnar and optimized for analytics.
JSON
Why wrong: JSON is a text format, not columnar.
Avro
Why wrong: Avro is row-oriented.
Parquet
Parquet is columnar and optimized for analytics.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
ORC
Parquet and ORC are columnar storage formats. JSON and CSV are row-oriented. Avro is row-oriented.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
CSV
Why it's wrong here
CSV is row-oriented.
ORC
Why this is correct
ORC is columnar and optimized for analytics.
Related concept
Read the scenario before looking for a memorised answer.
JSON
Why it's wrong here
JSON is a text format, not columnar.
Avro
Why it's wrong here
Avro is row-oriented.
Parquet
Why this is correct
Parquet is columnar and optimized for analytics.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
The correct answer is: ORC — Parquet and ORC are columnar storage formats. JSON and CSV are row-oriented. Avro is row-oriented.
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.
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 20, 2026
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.
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