Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A company is building a search application that requires grounding answers in their internal knowledge base. They want to use Vertex AI Search and Conversation with a custom datastore. Which configuration is essential to ensure the model only answers based on their documents?
⚠ Common exam trap
Google Cloud often tests the distinction between techniques that influence output style (temperature, streaming) versus those that control knowledge sources (grounding), leading candidates to confuse deterministic generation with factual grounding.
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
✓
Configure the answer generation to use grounding with the enterprise datastore as the source.
Vertex AI Search and Conversation provides a built-in grounding capability that explicitly ties answer generation to a specified enterprise datastore. By configuring grounding with the custom datastore as the source, the model is constrained to retrieve and synthesize answers exclusively from the indexed documents, preventing reliance on its parametric knowledge or external sources.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable streaming responses to get real-time answers.
Why it's wrong here
Streaming only changes how tokens are delivered to the client; it does not restrict generation to datastore content. Grounding requires datastore-backed retrieval with citations, not transport settings. Streaming is genuinely useful for perceived latency in chat interfaces, but here it leaves the model free to answer from its own parameters.
- ✗
Fine-tune the model on the company's documents.
Why it's wrong here
Fine-tuning alters model weights for style or task behaviour; it does not restrict answers to a document set, and grounding requires retrieval against the datastore at query time. It tempts because fine-tuning does inject domain knowledge, but it cannot guarantee citation-bound answers.
- ✓
Configure the answer generation to use grounding with the enterprise datastore as the source.
Why this is correct
Grounding binds generated answers to retrieved passages from the specified enterprise datastore, so the model cites and answers only from those documents rather than its pretrained knowledge. Pointing answer generation at that datastore as the grounding source is therefore essential to constrain responses to internal content.
- ✗
Set the model's temperature to 0 to make responses deterministic.
Why it's wrong here
Temperature 0 makes sampling greedy and repeatable, but determinism is unrelated to restricting answers to retrieved documents; the model can still hallucinate confidently. Temperature is for controlling randomness in creative or varied outputs. Grounding instead needs datastore retrieval with citations enforced at query time.
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