Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A large enterprise is using Vertex AI to deploy a generative AI model for internal document summarization. They need to ensure that the model's responses are based on the most current internal documents and that the model does not hallucinate. They also want to minimize latency and cost. Which feature of Vertex AI should they implement?
⚠ Common exam trap
The trap here is assuming that fine-tuning is the default solution for domain adaptation, when grounding is more effective for dynamic, up-to-date information.
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
✓
Grounding with Vertex AI Search
Grounding with Vertex AI Search is the correct feature because it enables the model to retrieve and cite relevant passages from a designated data store, ensuring responses are based on the latest internal documents. This approach reduces hallucinations and avoids the need for frequent fine-tuning, which can be costly and slow. It also supports low-latency responses by integrating retrieval with generation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Grounding with Vertex AI Search
Why this is correct
Grounding with Vertex AI Search allows the model to retrieve relevant information from a specified data store, such as internal documents, and use it to generate responses. This reduces hallucinations and ensures responses are based on current, proprietary data. It also minimizes the need for fine-tuning, which can be costly and time-consuming.
- ✗
Model evaluation
Why it's wrong here
Model evaluation assesses model performance metrics but does not influence the model's responses or ground them in current data. It is a monitoring and validation tool, not a feature that ensures factual accuracy or reduces hallucinations during inference.
- ✗
Batch prediction
Why it's wrong here
Batch prediction is used for processing large volumes of data asynchronously, not for interactive document summarization with low latency. It does not provide grounding in current documents and is unsuitable for real-time or near-real-time summarization needs.
- ✗
Model fine-tuning
Why it's wrong here
Fine-tuning can adapt a model to a specific domain but does not guarantee that responses are based on the most current documents. It requires retraining when documents change, leading to higher latency and cost. It is not the most efficient way to ensure up-to-date, grounded responses.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.