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Generative AI Leader Practice Question: A retail company uses Vertex AI Agent Builder to…
A retail company uses Vertex AI Agent Builder to create a virtual assistant for order tracking. Users frequently ask about delivery dates, but the assistant sometimes gives incorrect information. The team wants to improve accuracy without retraining the underlying model. Which technique should they apply?
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 Google Search or connect to a custom data store
Grounding with Google Search or enterprise data sources (like order databases) ensures the agent retrieves real-time, accurate information instead of relying solely on the model's training data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the temperature parameter for more deterministic outputs
Why it's wrong here
Raising temperature increases sampling randomness, producing more varied and less deterministic output, which worsens factual accuracy. Temperature tuning suits creative or diverse generation tasks. Grounding responses in retrieved order data via RAG addresses hallucinated delivery dates without retraining.
- ✗
Switch to a larger model size for better reasoning
Why it's wrong here
Switching model size changes the underlying model, contradicting the no-retraining constraint and adding cost without guaranteeing factual grounding. Larger models suit complex reasoning tasks. Retrieval-augmented generation supplies authoritative order data at inference time, correcting inaccurate delivery dates.
- ✗
Add more few-shot examples to the prompt template
Why it's wrong here
Few-shot examples shape output format and style but cannot supply live order-specific delivery data, so inaccuracies persist. Few-shot prompting suits teaching response structure for stable tasks. Retrieval-augmented generation fetches current order records, grounding answers in accurate delivery dates.
- ✓
Enable Grounding with Google Search or connect to a custom data store
Why this is correct
Grounding anchors the agent's responses to retrieved, verifiable sources — Google Search or a custom data store holding live order data — rather than the model's frozen parameters. This corrects stale delivery-date answers without retraining, satisfying the accuracy constraint.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.