- A
Avro
Avro supports schema evolution, is splittable, and BigQuery can read Avro files efficiently.
- B
CSV
Why wrong: CSV does not support schema evolution and is not efficient for BigQuery queries.
- C
Parquet
Parquet is columnar, splittable, supports schema evolution, and is highly optimized for BigQuery.
- D
JSON (newline-delimited)
Why wrong: JSON can be schemaless, but does not enforce schema evolution easily and is less performant for analytical queries.
- E
ORC
Why wrong: ORC is not natively supported by BigQuery as a source format.
Question 41 of 1,000
PDE Designing Data Processing Systems Practice Question
This PDE practice question tests your understanding of designing data processing systems. 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.
Your organization is designing a data lake on Google Cloud using Cloud Storage. You need to choose a file format for storing raw data that supports schema evolution, is splittable for parallel processing, and is optimized for query performance in BigQuery. Which TWO formats meet these requirements? (Choose 2.)
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
✓
Avro
Both Avro and Parquet support schema evolution (through schemas) and are splittable. Parquet is columnar and highly optimized for BigQuery performance. Avro is row-oriented but also splittable and supports schema evolution. CSV and JSON do not natively support schema evolution and are less performant for BigQuery. ORC is not natively supported by BigQuery.
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.
- ✓
Avro
Why this is correct
Avro supports schema evolution, is splittable, and BigQuery can read Avro files efficiently.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
CSV
Why it's wrong here
CSV does not support schema evolution and is not efficient for BigQuery queries.
- ✓
Parquet
Why this is correct
Parquet is columnar, splittable, supports schema evolution, and is highly optimized for BigQuery.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
JSON (newline-delimited)
Why it's wrong here
JSON can be schemaless, but does not enforce schema evolution easily and is less performant for analytical queries.
- ✗
ORC
Why it's wrong here
ORC is not natively supported by BigQuery as a source format.
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 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
Got this wrong? Here's your next step.
Identify which PDE 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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Designing Data Processing Systems — study guide chapter
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FAQ
Questions learners often ask
What does this PDE question test?
Designing Data Processing Systems — This question tests Designing Data Processing Systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Avro — Both Avro and Parquet support schema evolution (through schemas) and are splittable. Parquet is columnar and highly optimized for BigQuery performance. Avro is row-oriented but also splittable and supports schema evolution. CSV and JSON do not natively support schema evolution and are less performant for BigQuery. ORC is not natively supported by BigQuery.
What should I do if I get this PDE question wrong?
Identify which PDE 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.
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 →
Last reviewed: Jul 4, 2026
This PDE practice question is part of Courseiva's free Google Cloud 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 PDE exam.
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