- A
Increase the maximum output tokens to allow the model to generate more detailed summaries.
Why wrong: More tokens may increase the chance of hallucination.
- B
Implement a RAG pipeline using Vertex AI Search to retrieve relevant medical documents before generation.
RAG provides grounded, up-to-date context, reducing hallucinations significantly.
- C
Add more few-shot examples to the prompt for each generation.
Why wrong: Few-shot examples improve style but do not guarantee factual accuracy for unseen cases.
- D
Switch the base model to Gemini 1.5 Pro without additional changes.
Why wrong: Changing the base model does not address the lack of grounding and may still hallucinate.
Question 431 of 997
Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
This Generative AI Leader practice question tests your understanding of techniques to improve generative ai model output. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 healthcare startup has fine-tuned a Vertex AI PaLM 2 model on a dataset of medical records to generate patient summaries. The model produces fluent text but occasionally fabricates diagnoses not present in the input. The team has already tried increasing the training data size by 20% and adjusting the temperature from 0.7 to 0.2, but hallucinations persist. The summaries must be factually accurate for regulatory compliance. What should the team do next?
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
✓
Implement a RAG pipeline using Vertex AI Search to retrieve relevant medical documents before generation.
Option B is correct because implementing a Retrieval-Augmented Generation (RAG) pipeline with Vertex AI Search grounds the model's output in retrieved, authoritative medical documents. This directly addresses the root cause of hallucination—the model's reliance on its parametric memory—by providing factual context at inference time, which is far more effective for regulatory compliance than adjusting generation parameters or training data size alone.
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.
- ✗
Increase the maximum output tokens to allow the model to generate more detailed summaries.
Why it's wrong here
More tokens may increase the chance of hallucination.
- ✓
Implement a RAG pipeline using Vertex AI Search to retrieve relevant medical documents before generation.
Why this is correct
RAG provides grounded, up-to-date context, reducing hallucinations significantly.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Add more few-shot examples to the prompt for each generation.
Why it's wrong here
Few-shot examples improve style but do not guarantee factual accuracy for unseen cases.
- ✗
Switch the base model to Gemini 1.5 Pro without additional changes.
Why it's wrong here
Changing the base model does not address the lack of grounding and may still hallucinate.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume adjusting model parameters (temperature, tokens) or switching models will fix hallucinations, when in fact the core issue is the lack of external knowledge grounding, which only RAG or similar retrieval-based techniques can reliably address for factual accuracy.
Detailed technical explanation
How to think about this question
RAG works by embedding the user query and retrieving relevant chunks from a vector database (e.g., Vertex AI Vector Search) using cosine similarity, then injecting those chunks into the prompt as context. This forces the model to condition its generation on retrieved facts rather than its internal weights, effectively reducing hallucinations by providing a verifiable source. In a healthcare setting, this is critical because regulatory frameworks like HIPAA require that generated summaries be traceable to source records, which RAG enables through citation of retrieved documents.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Techniques to Improve Generative AI Model Output — This question tests Techniques to Improve Generative AI Model Output — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement a RAG pipeline using Vertex AI Search to retrieve relevant medical documents before generation. — Option B is correct because implementing a Retrieval-Augmented Generation (RAG) pipeline with Vertex AI Search grounds the model's output in retrieved, authoritative medical documents. This directly addresses the root cause of hallucination—the model's reliance on its parametric memory—by providing factual context at inference time, which is far more effective for regulatory compliance than adjusting generation parameters or training data size alone.
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.
About these practice questions
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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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