mediumMultiple Select
Generative AI Leader Practice Question: Deploy a GenAI code review assistant that…
A company wants to deploy a GenAI code review assistant that integrates into their existing Git workflow. They want to use a managed Google Cloud service to minimize operational overhead. Which TWO services should they consider? (Choose two.)
⚠ Common exam trap
Google often tests the distinction between managed CI/CD services (Cloud Build) and general-purpose compute (Cloud Run, Compute Engine), where candidates mistakenly choose Cloud Run because it is serverless, but it lacks native Git integration for automated code review triggers.
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
✓
Cloud Build
Cloud Build (A) is correct because it is a managed CI/CD service that natively integrates with Git repositories (via Cloud Source Repositories, GitHub, or Bitbucket) and can run build/review steps defined in cloudbuild.yaml, letting the code review assistant trigger automatically on commits and pull requests with minimal operational overhead. Vertex AI Agent Builder (D) is correct because it is a managed Google Cloud service for building, deploying, and orchestrating GenAI agents and applications, providing the LLM/reasoning layer needed for an AI code review assistant without managing infrastructure. Compute Engine (B) is not appropriate because it is raw IaaS VMs, which would require the company to manage OS patching, scaling, and runtime, contradicting the goal of minimizing operational overhead. Cloud Spanner (C) is a globally distributed relational database and does not provide CI/CD or GenAI agent capabilities. Cloud Run (E) is a managed serverless container platform, but by itself it is a generic compute runtime rather than the Git-integrated build pipeline or GenAI agent framework the scenario requires.
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 Build
Why this is correct
Cloud Build is a managed CI/CD service that natively triggers builds from Git repositories, satisfying the Git-workflow integration and low operational overhead constraints. It runs the review pipeline serverlessly, so the team avoids provisioning or patching build infrastructure themselves.
- ✗
Compute Engine
Why it's wrong here
Compute Engine provides raw IaaS virtual machines, leaving patching, scaling and runtime management to the customer, which contradicts the minimal-operational-overhead requirement. It is tempting because it hosts arbitrary workloads, and would suit lift-and-shift migration of a legacy application needing full OS control.
- ✗
Cloud Spanner
Why it's wrong here
Cloud Spanner is a globally distributed relational database, so it cannot host or serve the code review assistant's model or application logic. It is tempting because it is fully managed, and would be the right choice for a transactional application requiring horizontal scale and strong consistency across regions.
- ✓
Vertex AI Agent Builder
Why this is correct
Vertex AI Agent Builder provides a managed environment for building and orchestrating GenAI agents, satisfying the minimal-operational-overhead constraint. It supplies the reasoning and tool-calling layer that inspects code changes and posts review comments, without the team managing underlying model infrastructure.
- ✗
Cloud Run
Why it's wrong here
Cloud Run runs containerised request-serving workloads, but the scenario needs a managed service that ingests Git repositories and applies generative models to code. It is tempting because it is serverless and low-overhead, and would suit deploying a custom containerised API when no purpose-built managed service fits.
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
About these practice questions
Courseiva writes every Generative AI Leader question from scratch — 1,008 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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.