Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A healthcare company is building a chatbot to answer patient queries based on their medical documents stored in Cloud Storage. They want to minimize latency and ensure data residency in the EU. Which Vertex AI service should they use?
⚠ Common exam trap
Watch out — candidates often confuse Vertex AI Search (which grounds in private documents) with Vertex AI Agent Builder (which defaults to web search), or assume fine-tuning is necessary for domain-specific Q&A when retrieval-augmented generation (RAG) with document grounding is the correct approach for minimizing latency and ensuring data residency.
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 with document grounding
Vertex AI Search with document grounding is correct because it allows the chatbot to ground responses in the customer's own medical documents stored in Cloud Storage, ensuring low latency through optimized indexing and retrieval, while supporting data residency controls to keep data within the EU. This service is specifically designed for enterprise search and Q&A over private document repositories, making it ideal for healthcare use cases requiring compliance and fast responses.
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 Model Garden with fine-tuning
Why it's wrong here
Fine-tuning customizes models but does not index documents for retrieval.
- ✓
Vertex AI Search with document grounding
Why this is correct
Supports private document indexing and data residency controls.
- ✗
Vertex AI Agent Builder with web search
Why it's wrong here
Web search uses public data, not private documents.
- ✗
Vertex AI Codey APIs
Why it's wrong here
Codey is for code generation, not document Q&A.
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Senior Network & Security Engineer · founder of Courseiva
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