1Z0-1127-25 OCI Generative AI Service Practice Question
A team has fine-tuned a Cohere Command R model using T-Few on a dataset of 5,000 prompt/completion pairs. After deployment, they notice the model sometimes generates off-topic responses. Which action is most likely to improve response relevance without requiring new training data?
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 a preamble override with instructions to stay on topic
Preamble override allows setting a system message that guides the model's behavior, helping to keep responses on topic. Adjusting it is a low-cost intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the max_tokens
Why it's wrong here
Reducing output length does not address relevance; the model may still generate off-topic content within the limit.
- ✗
Increase the temperature to 1.5
Why it's wrong here
Higher temperature increases randomness, likely worsening off-topic responses.
- ✗
Increase the frequency_penalty
Why it's wrong here
Frequency penalty reduces repetition, but does not directly improve relevance.
- ✓
Set a preamble override with instructions to stay on topic
Why this is correct
Preamble override provides a system-level instruction that can steer the model's behavior toward relevance.
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