- A
Use a safety filter to block outputs containing harmful medical terminology.
Safety filters directly block harmful content at inference time.
- B
Implement RAG to retrieve verified medical information from trusted sources.
RAG grounds responses in authoritative sources, reducing hallucination.
- C
Fine-tune the model on a curated dataset of medical textbooks.
Fine-tuning improves domain accuracy and reduces harmful outputs.
- D
Include a disclaimer in the system instruction that the model is not a doctor.
Why wrong: A disclaimer does not prevent the generation of harmful advice.
- E
Set the temperature to a very high value to ensure diverse outputs.
Why wrong: High temperature increases randomness, potentially generating harmful content.
Question 631 of 997
Implement Safety Filters, RAG, and Fine-Tuning for Medical AI
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 development team is integrating a large language model into a healthcare application. They need to reduce the risk of generating harmful medical advice. Which THREE measures should they implement? (Choose three.)
Quick Answer
The answer is to implement a safety filter, fine-tune the model on a curated dataset of medical textbooks, and use retrieval-augmented generation (RAG) with trusted sources. These three measures work together to reduce the risk of generating harmful medical advice by blocking dangerous outputs, grounding the model in verified domain knowledge, and anchoring responses in authoritative references. On the Google Cloud Generative AI Leader exam, this question tests your understanding of how to apply safety measures for generative AI in healthcare, specifically distinguishing between architectural controls and superficial fixes. A common trap is choosing a high temperature setting, which increases randomness and toxicity, or relying solely on a disclaimer, which does not prevent harmful generation. Remember the mnemonic “SFR” for Safety filter, Fine-tuning, and RAG—these are the only measures that actively constrain model behavior rather than just warning users.
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
✓
Use a safety filter to block outputs containing harmful medical terminology.
Option A is correct because implementing a safety filter that blocks outputs containing harmful medical terminology directly mitigates the risk of generating dangerous advice. This acts as a post-processing guardrail, intercepting model outputs that include terms associated with diagnoses, dosages, or procedures that could lead to patient harm. It is a standard practice in high-stakes domains to layer such filters on top of the generative model.
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.
- ✓
Use a safety filter to block outputs containing harmful medical terminology.
Why this is correct
Safety filters directly block harmful content at inference time.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Implement RAG to retrieve verified medical information from trusted sources.
Why this is correct
RAG grounds responses in authoritative sources, reducing hallucination.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Fine-tune the model on a curated dataset of medical textbooks.
Why this is correct
Fine-tuning improves domain accuracy and reduces harmful outputs.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Include a disclaimer in the system instruction that the model is not a doctor.
Why it's wrong here
A disclaimer does not prevent the generation of harmful advice.
- ✗
Set the temperature to a very high value to ensure diverse outputs.
Why it's wrong here
High temperature increases randomness, potentially generating harmful content.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that disclaimers or system instructions alone are sufficient safety measures, when in fact they do not technically prevent the model from generating harmful content—only post-hoc filtering or architectural controls like RAG and fine-tuning can reduce the risk at the output level.
Detailed technical explanation
How to think about this question
Safety filters often use keyword-based or classifier-based detection (e.g., using a BERT-based toxicity model or a regex pattern for medication names and dosages) to block outputs before they reach the user. In practice, a filter might flag any output containing phrases like 'take 500mg of' or 'diagnosis: cancer' and either mask the response or return a pre-approved safe message. This is distinct from fine-tuning or RAG, which address the model's knowledge base but do not guarantee real-time output safety.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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: Use a safety filter to block outputs containing harmful medical terminology. — Option A is correct because implementing a safety filter that blocks outputs containing harmful medical terminology directly mitigates the risk of generating dangerous advice. This acts as a post-processing guardrail, intercepting model outputs that include terms associated with diagnoses, dosages, or procedures that could lead to patient harm. It is a standard practice in high-stakes domains to layer such filters on top of the generative model.
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.
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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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