1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A retail company uses OCI Generative AI to generate product descriptions. They observe the model occasionally produces biased content. Which technique should be applied to reduce bias in model outputs?
⚠ Common exam trap
Test-takers frequently confuse parameter tuning (like temperature or max_tokens) with content-level controls, assuming that reducing randomness or increasing output length can mitigate bias, when in fact bias is a training data issue that requires explicit instruction via prompt engineering to override.
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
✓
Apply prompt engineering with explicit instructions to avoid bias.
Prompt engineering allows you to explicitly instruct the model to avoid biased content, such as by including directives like 'Ensure the description is neutral and unbiased' in the system or user prompt. This technique directly influences the model's output generation without altering its underlying parameters, making it a targeted and effective approach for reducing bias in OCI Generative AI models.
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 max_tokens parameter.
Why it's wrong here
Increasing max_tokens does not mitigate bias; it may even increase the length of biased outputs.
- ✓
Apply prompt engineering with explicit instructions to avoid bias.
Why this is correct
Prompt engineering is the recommended approach to guide the model towards desired behavior and reduce bias.
- ✗
Reduce the model's inference temperature to 0.
Why it's wrong here
Reducing temperature to 0 makes outputs deterministic but does not inherently reduce bias; it may even amplify systematic biases.
- ✗
Use a different random seed for each request.
Why it's wrong here
Changing the random seed does not affect bias; it only varies output due to randomness.
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Senior Network & Security Engineer · founder of Courseiva
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