- A
BigQuery Omni
Why wrong: BigQuery Omni is for querying data in other clouds, not for external tables on GCS.
- B
BigQuery ML
Why wrong: BigQuery ML is for machine learning, not for external file querying.
- C
BigQuery external tables
External tables allow querying data directly from GCS without loading into BigQuery storage.
- D
BigQuery BI Engine
Why wrong: BI Engine is an in-memory analysis engine, not for external file querying.
Question 970 of 1,000
PDE Storing the Data Practice Question
This PDE practice question tests your understanding of storing the data. 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.
A company wants to use BigQuery to query data stored in Parquet files in Cloud Storage without loading the data into BigQuery. Which BigQuery feature should they use?
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
✓
BigQuery external tables
BigQuery external tables allow querying data stored in Cloud Storage (including Parquet files) directly without loading it into BigQuery storage. This feature uses a federated query engine that reads the data on the fly, supporting formats like Parquet, Avro, ORC, CSV, and JSON. Option C is correct because it directly addresses the requirement to query Parquet files in Cloud Storage without ingestion.
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.
- ✗
BigQuery Omni
Why it's wrong here
BigQuery Omni is for querying data in other clouds, not for external tables on GCS.
- ✗
BigQuery ML
Why it's wrong here
BigQuery ML is for machine learning, not for external file querying.
- ✓
BigQuery external tables
Why this is correct
External tables allow querying data directly from GCS without loading into BigQuery storage.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
BigQuery BI Engine
Why it's wrong here
BI Engine is an in-memory analysis engine, not for external file querying.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between features that query external data (external tables) versus features that process data within BigQuery (like BI Engine) or across clouds (Omni), leading candidates to confuse Omni's multi-cloud capability with external data access in the same cloud.
Detailed technical explanation
How to think about this question
When you create an external table pointing to Parquet files in Cloud Storage, BigQuery uses the BigQuery Storage API to read the files directly, leveraging columnar projection and predicate pushdown to minimize data scanned. Parquet's schema is automatically inferred, but you can also specify a schema explicitly. A subtle behavior is that external tables do not support DML operations (INSERT, UPDATE, DELETE) and have lower performance compared to native tables due to the lack of caching and indexing.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Storing the Data — study guide chapter
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FAQ
Questions learners often ask
What does this PDE question test?
Storing the Data — This question tests Storing the Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: BigQuery external tables — BigQuery external tables allow querying data stored in Cloud Storage (including Parquet files) directly without loading it into BigQuery storage. This feature uses a federated query engine that reads the data on the fly, supporting formats like Parquet, Avro, ORC, CSV, and JSON. Option C is correct because it directly addresses the requirement to query Parquet files in Cloud Storage without ingestion.
What should I do if I get this PDE 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
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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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