1Z0-1127-25 Using OCI Generative AI Service Practice Question
Which OCI Generative AI parameter controls the diversity of generated text by increasing the probability of less likely tokens?
⚠ Common exam trap
The OCI Generative AI exam often tests the distinction between temperature and top_p/top_k by asking which parameter directly scales probabilities to favor less likely tokens, leading candidates to confuse sampling strategies (top_p/top_k) with the logit scaling mechanism (temperature).
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
✓
temperature
The temperature parameter in OCI Generative AI controls the randomness of token selection by scaling the logits before applying the softmax function. A higher temperature (e.g., > 1.0) increases the probability of less likely tokens, making the output more diverse and creative, while a lower temperature (e.g., < 1.0) sharpens the distribution toward the most likely tokens, producing more deterministic and conservative text.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
frequency_penalty
Why it's wrong here
Frequency penalty reduces the likelihood of tokens that have already appeared.
- ✗
top_k
Why it's wrong here
Top_k limits the next token selection to the K most likely tokens.
- ✓
temperature
Why this is correct
Higher temperature increases randomness by scaling logits before softmax.
- ✗
top_p
Why it's wrong here
Top_p (nucleus sampling) selects from the smallest set of tokens whose cumulative probability exceeds p.
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