easyMultiple Choice
Generative AI Leader Practice Question: A data analyst needs to run a simple regression…
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?
⚠ Common exam trap
Watch out — 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.
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.
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
TensorFlow on Compute Engine requires exporting BigQuery data to a VM or reading it externally, so the model does not run in place. It suits custom deep-learning workloads needing GPU control. BigQuery ML instead executes regression through SQL directly against the stored table.
- ✓
BigQuery ML
Why this is correct
BigQuery ML executes regression directly inside BigQuery using SQL, so the data never leaves the platform — satisfying the no-movement constraint. Training and prediction run server-side against the stored table, unlike exporting to Vertex AI or a notebook, which would require extracting data first.
- ✗
Vertex AI Training
Why it's wrong here
Vertex AI Training is designed for distributed model training with custom containers or pre-built frameworks, not for executing regression models directly against BigQuery data without extraction. The correct service, BigQuery ML, uses SQL to train models in-place. This option tempts because Vertex AI Training does handle regression, but it requires moving data out of BigQuery into a Cloud Storage bucket for training, violating the stem’s constraint of no data movement.
- ✗
Google Colab
Why it's wrong here
Colab is a hosted notebook that queries BigQuery but trains the regression in its own runtime, so data leaves BigQuery. It suits exploratory prototyping with Python libraries. BigQuery ML runs CREATE MODEL natively, keeping training inside BigQuery without extraction.
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.