Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A small marketing agency wants to add an AI assistant that summarizes campaign briefs and drafts social posts. The developers have limited cloud experience and prefer a fully managed, serverless way to call Google's Gemini models without provisioning infrastructure. Which approach best meets this requirement?
⚠ Common exam trap
The trap here is assuming that any Google Cloud compute service, such as Cloud Run, is automatically the serverless answer for calling Gemini, when the managed Gemini API already removes the need to host anything.
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
✓
Use the Gemini API through Google AI Studio for rapid prototyping and API-key access
When the goal is simply to consume Gemini capabilities with minimal setup, the managed Gemini API accessed through Google AI Studio removes infrastructure concerns entirely. It supports summarization and drafting through straightforward API calls, letting a small team focus on prompts and application logic rather than serving stacks. Self-hosted or custom-trained alternatives introduce operational or data-science overhead the scenario does not require.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the Gemini API through Google AI Studio for rapid prototyping and API-key access
Why this is correct
Google AI Studio and the Gemini API provide a fully managed, serverless way to call Gemini models using an API key, with no infrastructure to provision. This suits a small team with limited cloud experience that wants quick access for summarization and drafting. It is the lowest-friction entry point for experimenting with and shipping lightweight Gemini-powered features.
- ✗
Deploy an open model on a Compute Engine VM with an attached GPU
Why it's wrong here
Running a model on a GPU virtual machine requires the team to manage drivers, serving frameworks, scaling and patching, which is the opposite of serverless. For a small agency with limited cloud experience, this adds significant operational burden and cost for a workload that only needs occasional text generation, so it does not meet the stated preference.
- ✗
Create a Vertex AI custom training job for a bespoke summarization model
Why it's wrong here
Custom training is intended for teams that need to fine-tune or build models from their own data with ML expertise. Here the requirement is simply to call an existing Gemini model for summaries and drafts, so training a bespoke model adds cost, data preparation work and lifecycle management without providing value for this scenario.
- ✗
Build a Cloud Run service that hosts a self-managed transformer container
Why it's wrong here
Cloud Run is serverless for containers, but the team would still have to build, containerize and maintain a model-serving image, manage memory limits and handle cold starts for a large model. That is far more work than calling a managed Gemini endpoint, and it does not align with the limited-experience, minimal-infrastructure requirement.
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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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.