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Generative AI Leader Practice Question: Integrate generative AI into their existing CRM…
A company wants to integrate generative AI into their existing CRM workflow to draft personalized email responses. They have limited engineering resources. Which two approaches should they consider? (Choose TWO)
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 Vertex AI API with a low-code integration platform (e.g., Apigee)
Using Gemini API via Apps Script is a lightweight integration, and using Vertex AI API with a low-code tool like Apigee or Cloud Functions can also minimize engineering effort. Building a custom UI or fine-tuning is resource-intensive.
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 Vertex AI API with a low-code integration platform (e.g., Apigee)
Why this is correct
Low-code platforms reduce the need for custom coding.
- ✗
Fine-tune a model on historical email data to ensure brand voice
Why it's wrong here
Fine-tuning adds complexity and may not be necessary for drafting emails; prompt engineering may suffice.
- ✓
Use Gemini API via Google Apps Script to add a custom menu in the CRM
Why this is correct
Apps Script allows easy integration with Google Workspace and external CRMs via APIs.
- ✗
Deploy a dedicated GPU cluster for inference
Why it's wrong here
Deploying a dedicated GPU cluster for inference addresses the computational load of running large models locally, but the scenario explicitly states limited engineering resources. Managing GPU infrastructure—including orchestration, scaling, and maintenance—requires specialised DevOps skills the company lacks. This option is tempting because it offers full control over latency and data privacy, and would be correct if the company had an in-house ML engineering team and needed to avoid third-party API dependencies.
- ✗
Build a custom web UI for the assistant from scratch
Why it's wrong here
Custom UI development requires significant engineering resources.
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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About these practice questions
Courseiva writes every Generative AI Leader question from scratch — 683 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.