- A
TensorFlow on Compute Engine
Why wrong: Requires data export and custom infrastructure.
- B
BigQuery ML
BigQuery ML enables ML via SQL on BigQuery data.
- C
Vertex AI Training
Why wrong: Vertex AI Training requires moving data to GCS and is more complex.
- D
Google Colab
Why wrong: Colab is a notebook environment, not integrated with BigQuery SQL.
Question 607 of 997
Generative AI Leader Google AI Ecosystem and Strategy Practice Question
This Generative AI Leader practice question tests your understanding of google ai ecosystem and strategy. 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 data analyst needs to run a simple regression model directly on data stored in BigQuery without moving data to another platform. Which service 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 ML
BigQuery ML (B) is correct because it allows users to create and execute machine learning models using standard SQL syntax directly on data stored in BigQuery, without needing to export data to a separate platform. This service is specifically designed for running regression, classification, and other models natively within BigQuery, leveraging its serverless architecture and built-in ML capabilities.
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.
- ✗
TensorFlow on Compute Engine
Why it's wrong here
Requires data export and custom infrastructure.
- ✓
BigQuery ML
Why this is correct
BigQuery ML enables ML via SQL on BigQuery data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Vertex AI Training
Why it's wrong here
Vertex AI Training requires moving data to GCS and is more complex.
- ✗
Google Colab
Why it's wrong here
Colab is a notebook environment, not integrated with BigQuery SQL.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Vertex AI Training (a full-featured ML platform) with BigQuery ML, not realizing that Vertex AI requires data export and more setup, while BigQuery ML is purpose-built for in-database modeling with minimal overhead.
Detailed technical explanation
How to think about this question
BigQuery ML uses the `CREATE MODEL` statement with a `model_type` parameter (e.g., `'LINEAR_REG'` for linear regression) to train models directly on BigQuery tables, leveraging distributed processing via the same infrastructure that powers BigQuery queries. Under the hood, it uses a variant of stochastic gradient descent or analytical solvers, and the model is stored as a BigQuery resource, allowing for batch predictions with `ML.PREDICT` without data egress. A real-world scenario is a marketing analyst building a churn prediction model on terabytes of customer data without leaving the BigQuery console, reducing latency and cost.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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 |
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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Google AI Ecosystem and Strategy — study guide chapter
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Google AI Ecosystem and Strategy — This question tests Google AI Ecosystem and Strategy — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: BigQuery ML — BigQuery ML (B) is correct because it allows users to create and execute machine learning models using standard SQL syntax directly on data stored in BigQuery, without needing to export data to a separate platform. This service is specifically designed for running regression, classification, and other models natively within BigQuery, leveraging its serverless architecture and built-in ML capabilities.
What should I do if I get this Generative AI Leader 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
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 →
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Last reviewed: Jul 4, 2026
This Generative AI Leader 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 Generative AI Leader exam.
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