- A
CSV
Why wrong: CSV is row-oriented.
- B
ORC
ORC is columnar and optimized for analytics.
- C
JSON
Why wrong: JSON is a text format, not columnar.
- D
Avro
Why wrong: Avro is row-oriented.
- E
Parquet
Parquet is columnar and optimized for analytics.
Question 1,230 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 data formats are columnar and optimized for analytics queries in Amazon S3?
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
✓
ORC
ORC (Optimized Row Columnar) is a columnar storage format that stores data in a column-oriented manner, enabling efficient compression and predicate pushdown for analytics queries on Amazon S3. It is designed for high-performance read operations in big data frameworks like Apache Hive and Spark, making it ideal for aggregation and filtering workloads.
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.
- ✗
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
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'binary format' (Avro) or 'structured text' (JSON, CSV) with columnar optimization, failing to recognize that only columnar formats like ORC and Parquet provide the compression and predicate pushdown needed for analytics at scale.
Detailed technical explanation
How to think about this question
Columnar formats like ORC and Parquet store data by columns rather than rows, allowing queries to read only the columns needed, drastically reducing I/O. Under the hood, ORC uses lightweight compression (e.g., dictionary encoding, run-length encoding) and indexes (e.g., stripe-level statistics) to skip irrelevant data blocks, which is critical for S3-based analytics where network latency and data transfer costs are significant. In real-world scenarios, using ORC or Parquet can reduce query scan sizes by 70-90% compared to row-oriented formats like CSV or JSON.
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 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.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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: ORC — ORC (Optimized Row Columnar) is a columnar storage format that stores data in a column-oriented manner, enabling efficient compression and predicate pushdown for analytics queries on Amazon S3. It is designed for high-performance read operations in big data frameworks like Apache Hive and Spark, making it ideal for aggregation and filtering workloads.
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.
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 →
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Last reviewed: Jul 4, 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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