1Z0-1127-25 Fundamentals of Large Language Models Practice Question
An enterprise is deploying a chat application using a large language model. Users report that the model sometimes generates toxic or biased responses. Which best practice should be applied to mitigate this issue?
⚠ Common exam trap
Oracle often tests the misconception that adjusting model parameters (like temperature or max_tokens) can fix safety issues, when in reality, safety requires external guardrails like content filters.
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 content filtering layer using a safety classifier to detect and block toxic outputs.
Implementing a content filtering layer using a safety classifier is a proven best practice to detect and block toxic or biased outputs in real-time. This approach acts as a guardrail, intercepting harmful responses before they reach users, and is independent of the model's internal parameters or 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.
- ✗
Use few-shot prompting with examples of toxic responses so the model learns to avoid them.
Why it's wrong here
Few-shot with toxic examples can inadvertently reinforce toxic patterns.
- ✗
Increase the max_tokens parameter to allow the model more context to correct itself.
Why it's wrong here
More tokens do not reduce toxicity; the model may still generate toxic content.
- ✗
Disable the temperature parameter to make outputs deterministic.
Why it's wrong here
Temperature affects randomness, not toxicity.
- ✓
Implement a content filtering layer using a safety classifier to detect and block toxic outputs.
Why this is correct
Safety classifiers directly filter toxic content.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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