PDE Storing the Data Practice Question
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?
⚠ Common exam trap
Google 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.
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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BigQuery Omni
Why it's wrong here
BigQuery Omni queries data in AWS S3 and Azure Blob Storage, not Cloud Storage. It is tempting because it also queries external data without loading, and would be correct when the Parquet files reside in another cloud provider's object store rather than GCS.
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BigQuery ML
Why it's wrong here
BigQuery ML builds and runs machine-learning models inside BigQuery using SQL; it cannot query external Parquet files. It is tempting because it extends BigQuery to predictive analytics, and would be the right choice when training or scoring models on data already in BigQuery tables.
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BigQuery external tables
Why this is correct
External tables let BigQuery query Parquet files in Cloud Storage directly, reading the data in place without ingestion. This satisfies the no-loading constraint, since no data is copied into BigQuery's native storage and queries run against the external source.
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BigQuery BI Engine
Why it's wrong here
BI Engine is an in-memory analysis cache that accelerates queries against BigQuery-managed tables; it does not read Parquet files in Cloud Storage. It is tempting because it speeds up dashboards, and would be correct when sub-second latency is needed on existing BigQuery datasets.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.