1Z0-1127-25 LLM Fundamentals Practice Question
A practitioner is using a Cohere Command model on OCI for a translation task. They notice that the output is often incomplete and cuts off mid-sentence. Which parameter should they adjust to address this?
⚠ Common exam trap
The 1Z0-1127 exam often tests the misconception that temperature or top-p controls output length, when in fact they only affect token selection probability and diversity, not the maximum number of tokens generated.
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
✓
Max tokens
The 'Max tokens' parameter controls the maximum length of the generated output. When a model cuts off mid-sentence, it means the token limit has been reached before the model could complete its response. Increasing this value allows the model to generate more tokens, thus completing the translation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Temperature
Why it's wrong here
Temperature controls randomness, not length.
- ✓
Max tokens
Why this is correct
Max tokens sets the maximum length of the generated output.
- ✗
Frequency penalty
Why it's wrong here
Frequency penalty discourages repetition, not truncation.
- ✗
Top-p
Why it's wrong here
Top-p affects diversity, not length.
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