Generative AI Leader Fundamentals of Generative AI Practice Question
A financial services company is using a large language model on Vertex AI to generate summaries of earnings reports. They notice that the model sometimes includes information not present in the source reports, leading to compliance risks. They want to reduce hallucinations by ensuring the model's output is grounded in the provided documents. Which technique should they implement?
⚠ Common exam trap
The trap here is assuming fine-tuning eliminates hallucinations; it can adapt style but does not guarantee grounding in a specific input document.
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
✓
Retrieval-augmented generation (RAG)
Retrieval-augmented generation (RAG) is the most effective technique to ground model outputs in specific documents. By retrieving relevant passages from the earnings reports and including them in the prompt, the model is constrained to generate summaries based on that evidence, significantly reducing hallucinations and compliance risks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Using a smaller model
Why it's wrong here
A smaller model may have less capacity to hallucinate, but it can also be less accurate and may still generate unsupported content. It does not provide grounding in the source documents. The size of the model is not a reliable solution for reducing hallucinations in a compliance context.
- ✗
Increasing the temperature
Why it's wrong here
Increasing the temperature makes the model more creative and diverse, which would likely increase hallucinations rather than reduce them. It is the opposite of what is needed for factual grounding. Higher temperature leads to more random token selection, which is undesirable for compliance-sensitive summaries.
- ✓
Retrieval-augmented generation (RAG)
Why this is correct
RAG involves retrieving relevant passages from the source documents and providing them as context to the model, which reduces hallucinations by grounding the generation in factual content. This directly addresses the compliance risk by ensuring the summary is based on the earnings reports, not the model's internal knowledge.
- ✗
Fine-tuning the model on earnings reports
Why it's wrong here
Fine-tuning can adapt the model's style and knowledge to a domain, but it does not guarantee that the model will only use information from a specific input document. The model may still hallucinate or mix in learned facts. RAG is more effective for grounding in a specific set of documents at inference time.
Go deeper
Related to this question
About these practice questions
Courseiva writes every Generative AI Leader question from scratch — 1,008 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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.