1Z0-1127-25 LLM Fundamentals Practice Question
An OCI customer is deploying a chatbot using a pre-trained LLM. They are concerned about the model generating biased or harmful content. Which TWO strategies should they implement as part of their responsible AI approach? (Choose two.)
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
✓
Set up a human-in-the-loop review for sensitive queries
Content filtering and human review are direct mitigations. Training from scratch is impractical; modifying sampling does not address bias; increasing context window is irrelevant.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Train the model from scratch on a curated dataset
Why it's wrong here
Training from scratch is expensive and not a quick mitigation.
- ✗
Increase the context window to include more examples
Why it's wrong here
Context window does not address bias or harm.
- ✓
Set up a human-in-the-loop review for sensitive queries
Why this is correct
Human review ensures oversight for high-risk interactions.
- ✗
Use top-k sampling with k=1
Why it's wrong here
Greedy decoding does not reduce bias; it may even amplify it.
- ✓
Implement a content filtering layer to detect and block harmful outputs
Why this is correct
Content filtering catches problematic outputs before they reach users.
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