Cloud Digital Leader Google Cloud Products and Services Practice Question
Which Google Cloud service is a fully managed, serverless data warehouse for analytics with built-in ML capabilities (e.g., BigQuery ML)?
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
BigQuery is a serverless data warehouse that supports standard SQL, scales automatically, and includes BigQuery ML for creating ML models using SQL.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud SQL
Why it's wrong here
Cloud SQL is a relational database service for transactional workloads (OLTP), not a data warehouse, so it lacks the columnar storage and distributed query engine needed for large-scale analytics. It also does not offer built-in ML capabilities like BigQuery ML, which executes models directly on SQL queries. Cloud SQL is tempting because it provides a familiar SQL interface and managed database operations, making it the correct choice for migrating an on-premises MySQL or PostgreSQL application to the cloud without redesigning the schema.
- ✗
Firestore
Why it's wrong here
Firestore is a fully managed NoSQL document database optimized for real-time synchronization, offline support, and mobile/web application backends. Its document/collection model is designed for operational, transactional workflows, not for running complex analytical joins and aggregations over large datasets. Because it lacks columnar storage and a distributed query engine for petabyte-scale scans, it cannot function as a serverless data warehouse.
- ✗
Cloud Spanner
Why it's wrong here
Cloud Spanner is a globally distributed relational database service built for strong consistency and horizontally scalable OLTP workloads, such as financial transactions or inventory systems. It does not employ columnar storage nor a query execution engine optimized for large-scale analytical scans, so it is not suited to data warehouse workloads. Moreover, it is not serverless—you must provision instances and nodes, meaning capacity management is required rather than automatic scaling.
- ✓
BigQuery
Why this is correct
BigQuery is a serverless data warehouse that uses columnar storage and a distributed query engine to run ANSI SQL analytics on petabytes of data without provisioning infrastructure. It separates storage and compute, allowing independent scaling, and introduces BigQuery ML to create and execute machine learning models directly on SQL queries. The service also integrates with Dataflow, Dataproc, and Looker for end-to-end analytics pipelines.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
Go deeper
Related to this question
Learn chapter
Cloud Digital Transformation
Key term
Serverless
Serverless is a cloud computing model where the cloud provider manages the servers, and you only pay for the actual compute time your code uses, without having to worry about provisioning or maintaining infrastructure.
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
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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.