1Z0-1127-25 Using OCI Generative AI Service Practice Question
A company deployed OCI Generative AI for a customer service chatbot. They are using the Cohere command model. The chatbot is generating responses that are too brief and often cut off mid-sentence. They have limited budget. What should they do?
⚠ Common exam trap
A common misconception is that temperature adjustments can fix response length issues, when in fact temperature only controls randomness and creativity, not the output token limit.
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
✓
Increase max tokens to 1024.
The chatbot's responses are too brief and cut off mid-sentence, which indicates the model is hitting the maximum token limit for generation. Increasing the max tokens to 1024 allows the Cohere command model to produce longer, complete responses without truncation. This is the most direct and cost-effective fix for the described symptom.
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 max tokens to 1024.
Why this is correct
Increasing max tokens gives the model more room to complete its response.
- ✗
Decrease the temperature to 0.2.
Why it's wrong here
Lower temperature makes output more deterministic but does not lengthen responses.
- ✗
Increase the temperature to 0.9.
Why it's wrong here
Higher temperature increases randomness, not output length.
- ✗
Use a different base model like Llama.
Why it's wrong here
Changing models incurs additional effort and cost, and may not directly solve the truncation issue.
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