- A
Deploy the agent to a Vertex AI endpoint
Why wrong: Endpoint deployment is for serving the model, not connecting to external data.
- B
Fine-tune a Gemini model on the knowledge base
Why wrong: Fine-tuning would adapt the model but is less efficient for dynamic retrieval; grounding is designed for this use case.
- C
Enable grounding with a data store
Grounding allows the agent to retrieve information from a data store created from Cloud Storage documents.
- D
Configure a safety filter to block irrelevant queries
Why wrong: Safety filters block harmful content, they do not retrieve information.
Question 75 of 997
Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
This Generative AI Leader practice question tests your understanding of google cloud's generative ai offerings. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company is building a customer support chatbot using Vertex AI Agent Builder. They want the agent to answer questions based on internal knowledge base documents stored in Cloud Storage. Which feature should they configure to ensure the agent can retrieve relevant information from these documents?
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
✓
Enable grounding with a data store
Option C is correct because Vertex AI Agent Builder uses grounding to connect the agent to external data sources, such as documents stored in Cloud Storage. By enabling grounding with a data store, the agent can retrieve and reference relevant information from the knowledge base documents in real time, ensuring accurate and context-aware responses without requiring model retraining.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy the agent to a Vertex AI endpoint
Why it's wrong here
Endpoint deployment is for serving the model, not connecting to external data.
- ✗
Fine-tune a Gemini model on the knowledge base
Why it's wrong here
Fine-tuning would adapt the model but is less efficient for dynamic retrieval; grounding is designed for this use case.
- ✓
Enable grounding with a data store
Why this is correct
Grounding allows the agent to retrieve information from a data store created from Cloud Storage documents.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Configure a safety filter to block irrelevant queries
Why it's wrong here
Safety filters block harmful content, they do not retrieve information.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often confuse fine-tuning with grounding. They mistakenly choose fine-tuning (Option B) assuming the model must be retrained on the knowledge base, but the correct approach for retrieval-based Q&A is grounding with a data store.
Detailed technical explanation
How to think about this question
Grounding in Vertex AI Agent Builder leverages a data store that indexes documents from Cloud Storage using the Document AI or Vertex AI Search APIs, enabling semantic search and retrieval. Under the hood, the agent uses embeddings and vector search to match user queries with relevant document chunks, then passes them as context to the Gemini model for response generation. In a real-world scenario, this allows the agent to answer questions about product manuals or policy documents without needing to retrain the model, even as the documents are updated.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Google Cloud's Generative AI Offerings — This question tests Google Cloud's Generative AI Offerings — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable grounding with a data store — Option C is correct because Vertex AI Agent Builder uses grounding to connect the agent to external data sources, such as documents stored in Cloud Storage. By enabling grounding with a data store, the agent can retrieve and reference relevant information from the knowledge base documents in real time, ensuring accurate and context-aware responses without requiring model retraining.
What should I do if I get this Generative AI Leader question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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