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Generative AI Leader Practice Question: A retail company wants to build an internal…
A retail company wants to build an internal knowledge base chatbot using Vertex AI. They need to ensure the chatbot only answers from approved company documents and can handle updates without retraining. Which TWO components should they include? (Choose 2)
⚠ Common exam trap
The trap is assuming fine-tuning is needed to make a model 'know' company documents; the exam expects you to recognize RAG plus a vector store as the no-retrain, grounded-answer pattern.
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
✓
RAG Engine to connect the chatbot to a document index
Option A is correct because RAG Engine (Retrieval-Augmented Generation) is the Vertex AI component that grounds a chatbot's responses in an external document index, ensuring answers come only from approved company documents rather than the model's parametric knowledge. Option B is correct because a vector store such as Vertex AI Vector Search is required to embed and index the documents so that relevant chunks can be retrieved at query time, and the index can be updated with new documents without retraining the underlying model. Together, RAG Engine plus a vector store satisfy both requirements: grounded answers from approved sources and updates via index refresh instead of retraining. Option C is not appropriate because fine-tuning bakes document knowledge into model weights, which requires retraining to reflect updates and does not restrict answers to approved documents. Option D is not sufficient because Model Garden only lets you discover and select pre-trained or foundation models; it provides no grounding or document retrieval capability. Option E is incorrect because Apps Script is a Google Workspace automation tool and is not used to build or update a Vertex AI vector index.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
RAG Engine to connect the chatbot to a document index
Why this is correct
RAG Engine retrieves relevant passages from an indexed corpus of approved company documents and supplies them as grounding context, so answers stay within that content. Because the index updates independently, documents can change without retraining the underlying model.
- ✓
A vector store (e.g., Vertex AI Vector Search) to index the documents
Why this is correct
Grounding responses in approved documents requires retrieval-augmented generation: the vector store indexes document embeddings so the model retrieves relevant chunks at query time. Updating the index reflects new or changed documents immediately, satisfying the no-retraining constraint.
- ✗
Fine-tuned model on company documents
Why it's wrong here
Fine-tuning bakes company documents into model weights, so every content update requires retraining and answers are not restricted to approved sources. Fine-tuning is correct when the goal is teaching a consistent style or domain vocabulary rather than dynamic retrieval.
- ✗
Model Garden to select a pre-trained model
Why it's wrong here
Model Garden only supplies a selectable pre-trained model; it provides no document grounding or retrieval, so answers would come from parametric knowledge rather than approved sources. It is the right component when the task is simply choosing a foundation model for a new application.
- ✗
Apps Script to update the document index
Why it's wrong here
Apps Script automates Google Workspace applications; it cannot build or refresh a Vertex AI vector index, so document updates would never reach retrieval. It is the right tool for scripting Sheets or Docs workflows, not for grounding a chatbot.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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