Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A global logistics company wants to build a generative AI assistant that can answer operational questions by retrieving information from internal PDF and HTML documents stored in Cloud Storage. They need a managed, serverless retrieval-augmented generation (RAG) capability that requires minimal infrastructure management and integrates with Vertex AI. Which Google Cloud service should they use?
⚠ Common exam trap
The trap here is assuming that any Google Cloud service that can store embeddings, such as Cloud SQL with pgvector, is a suitable RAG solution, when the requirement is a managed, serverless retrieval service.
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
✓
Vertex AI Search
Vertex AI Search delivers a fully managed, serverless retrieval-augmented generation pipeline that ingests documents from Cloud Storage, builds an index, and grounds Gemini responses without requiring the team to manage embeddings or vector databases. The other services are either feature stores, self-managed vector databases, or analytics tools that do not provide end-to-end RAG for unstructured content.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Vertex AI Feature Store
Why it's wrong here
Vertex AI Feature Store is designed to serve and manage structured ML features for training and online prediction, not to index unstructured documents or provide retrieval-augmented generation. It lacks document parsing, chunking, and semantic search over PDFs and HTML, so it cannot ground a generative assistant in the company's operational documents.
- ✗
Cloud SQL with pgvector
Why it's wrong here
While Cloud SQL with pgvector can store and query embeddings, it is not a managed RAG service. The logistics company would have to build and maintain embedding generation, chunking, and retrieval logic themselves, which contradicts the requirement for minimal infrastructure management and serverless operation.
- ✓
Vertex AI Search
Why this is correct
Vertex AI Search is a fully managed, serverless search and retrieval service that supports RAG by grounding Gemini responses in enterprise data from Cloud Storage, websites, and other sources. It handles indexing, chunking, and retrieval without requiring the team to manage vector databases or embeddings pipelines, making it the ideal fit for a low-ops RAG solution.
- ✗
BigQuery ML
Why it's wrong here
BigQuery ML allows training and inference of ML models using SQL, but it does not provide document ingestion, chunking, or semantic retrieval over PDFs and HTML for RAG. It is optimized for structured data analytics rather than grounding generative AI responses in unstructured enterprise content, so it does not meet the scenario's needs.
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.