AI-102 Implement generative AI solutions Practice Question
Exhibit
{
"deployment": {
"model": "gpt-35-turbo",
"modelVersion": "0613",
"scaleSettings": {
"scaleType": "Standard"
},
"properties": {
"maxTokens": 4096,
"temperature": 0.7,
"topP": 0.95,
"frequencyPenalty": 0,
"presencePenalty": 0
}
}
}Refer to the exhibit. You have deployed a GPT-3.5 Turbo model in Azure OpenAI Service with the shown configuration. Users report that the model generates responses that are too repetitive. You need to reduce repetition. Which parameter should you modify?
⚠ Common exam trap
Watch out — candidates often confuse presencePenalty with frequencyPenalty, assuming both address repetition equally, but presencePenalty specifically targets repetition of already-seen tokens in the current response, making it the correct choice for this scenario.
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 presencePenalty to 0.5
Increasing the presencePenalty parameter penalizes tokens that have already appeared in the generated text, encouraging the model to introduce new topics and reduce repetition. In Azure OpenAI Service, presencePenalty directly influences the logit scores of previously seen tokens, making them less likely to be selected again, which addresses the user's complaint of overly repetitive responses.
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 presencePenalty to 0.5
Why this is correct
Presence penalty reduces the likelihood of repeating any token that has appeared, reducing repetition.
- ✗
Increase frequencyPenalty to 0.5
Why it's wrong here
Frequency penalty reduces repetition of frequent tokens, but presence penalty is more effective for this scenario.
- ✗
Increase temperature to 1.0
Why it's wrong here
Increasing temperature increases randomness, not reducing repetition.
- ✗
Decrease topP to 0.5
Why it's wrong here
Decreasing topP reduces diversity, not repetition.
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