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Generative AI Leader Practice Question: Use GenAI to create a knowledge base assistant…
A company wants to use GenAI to create a knowledge base assistant that can answer questions from internal documentation. They need the assistant to always have access to the latest documents without retraining. Which TWO services should they combine? (Choose 2)
⚠ Common exam trap
Generative AI Leader often tests the confusion between model fine-tuning and retrieval-augmented generation; candidates may think retraining is needed for updated documents, but the correct approach is to combine an agent framework with a vector search service for dynamic retrieval.
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 Agent Builder
Vertex AI Agent Builder (B) is correct because it provides the orchestration and grounding layer for building conversational search and RAG-style assistants over enterprise content, letting the agent retrieve from a connected data store at query time rather than relying on model retraining. Vertex AI Vector Search (E) is correct because it supplies the scalable nearest-neighbor index that stores embeddings of the internal documents and returns the most relevant chunks for each question, so newly added or updated documents can be indexed and served immediately. Together they satisfy the requirement that the assistant always accesses the latest documents without retraining, since retrieval happens dynamically against the vector index. Vertex AI Pipelines (A) is for orchestrating ML workflows such as training, tuning, and batch processing, not for serving up-to-date document retrieval to an assistant. Vertex AI Model Garden (C) is a catalog for discovering and deploying foundation models, which does not by itself provide document indexing or retrieval. Duet AI in Google Docs (D) is an end-user writing assistant inside Docs and cannot serve as the retrieval backend for a custom knowledge base assistant.
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 Pipelines
Why it's wrong here
Vertex AI Pipelines orchestrates ML workflows such as training, tuning and deployment; it does not perform query-time document retrieval. The scenario demands grounding on current documents without retraining, which pipelines cannot supply. Pipelines would be correct for automating model training or batch inference workflows instead.
- ✓
Vertex AI Agent Builder
Why this is correct
Vertex AI Agent Builder orchestrates the assistant, grounding responses in retrieved documents via its RAG pipeline. This satisfies the no-retraining constraint because updated documents are indexed and retrieved at query time rather than baked into model weights.
- ✗
Vertex AI Model Garden
Why it's wrong here
Model Garden supplies pre-trained and foundation models for selection and deployment; it does not index or retrieve your internal documents at query time. Retrieval-augmented generation needs a vector search or grounding service. Model Garden is the right pick when choosing or fine-tuning a base model, not for keeping answers current.
- ✗
Duet AI in Google Docs
Why it's wrong here
Duet AI in Google Docs assists authors within documents; it cannot serve as a retrieval backend for a custom assistant. Freshness without retraining requires query-time retrieval from an indexed corpus. Duet AI is correct when the goal is drafting and editing text inside Workspace, not building an external knowledge base.
- ✓
Vertex AI Vector Search
Why this is correct
Vertex AI Vector Search supplies the retrieval layer: documents are embedded and indexed, so the assistant queries current content at inference time rather than relying on weights frozen during training. This satisfies the requirement for always-current documents without retraining, complementing a generative model that composes the final answer.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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