Cloud Digital Leader Why Cloud Technology Can Transform Business Practice Question
A data analytics firm wants to query data across Cloud Storage and BigQuery without moving the data. They need a single SQL interface. Which Google Cloud service enables this?
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 (federated queries)
BigQuery allows querying external data sources like Cloud Storage via external tables (federated queries) without loading data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Dataproc with Spark SQL
Why it's wrong here
Dataproc with Spark SQL is a managed Apache Spark service that can certainly read from Cloud Storage, but it requires standing up a cluster, configuring autoscaling and cluster policies, and initializing the Spark session before any query can run. This operational overhead makes it a heavier solution than a native SQL interface, and the user would not be working within BigQuery's serverless SQL environment. The question calls for querying data 'across' cloud storage with minimal friction, which Dataproc does not provide as a single SQL interface.
- ✓
BigQuery external tables (federated queries)
Why this is correct
BigQuery external tables, also known as federated tables, let you create a table definition that points to one or more files in Cloud Storage and query those files with standard BigQuery SQL without loading data into BigQuery storage. You can manually specify the schema or let BigQuery auto-detect it, and you can use wildcards to span multiple objects in a bucket. This directly satisfies the requirement to query data across cloud storage while keeping BigQuery as the serverless query engine.
- ✗
Cloud SQL federated queries
Why it's wrong here
Cloud SQL federated queries allow a Cloud SQL instance to run SELECT statements against data hosted in BigQuery or other MySQL-compatible sources, but they do not turn Cloud SQL into a serverless query engine for raw files in Cloud Storage. You would still need to load the files into Cloud SQL or BigQuery, which defeats the purpose of directly querying cloud storage without loading. The service is also a traditional RDBMS with connection and instance management overhead.
- ✗
BigQuery Omni
Why it's wrong here
BigQuery Omni is specifically designed for multi-cloud analytics, allowing BigQuery queries to run against data stored in AWS S3 and Azure Blob Storage using the same SQL dialect. It does not apply to Google Cloud Storage, because GCS is already a first-class native storage location for BigQuery and is handled by standard BigQuery tables or external tables. Choosing Omni here would be both unnecessary and incorrect, as it adds cross-cloud connection complexity rather than simplifying direct queries to GCS.
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Cloud Digital Transformation
Key term
Google Cloud
Google Cloud is a suite of cloud computing services offered by Google that provides infrastructure, platform, and software solutions over the internet.
Key term
BigQuery
BigQuery is a fully managed, serverless data warehouse on Google Cloud that lets you run fast SQL queries on massive datasets without managing any infrastructure.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This GCDL 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 GCDL exam.