The correct parameter to modify is presence_penalty, which should be increased to 0.5 to reduce repetition in GPT-3.5 Turbo responses. Presence_penalty works by penalizing tokens that have already appeared in the generated text, thereby discouraging the model from reusing the same words or topics and promoting more diverse output. On the AI-102 exam, this question tests your understanding of how Azure OpenAI Service parameters control generation behavior, often appearing as a scenario where you must distinguish between temperature, top_p, frequency_penalty, and presence_penalty. A common trap is confusing frequency_penalty, which reduces repetition of specific tokens based on frequency, with presence_penalty, which penalizes any token that has appeared at all—making presence_penalty more effective for reducing topical repetition. Remember the memory tip: “Presence prevents repeats; frequency fights frequency.”
AI-102 Implement generative AI solutions Practice Question
This AI-102 practice question tests your understanding of implement generative ai solutions. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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
Option D is correct because presence_penalty reduces the likelihood of repeating tokens that have already appeared. Option A is wrong because temperature controls creativity. Option B is wrong because topP controls nucleus sampling. Option C is wrong because frequency_penalty is set to 0 and could be increased, but presence_penalty is more effective for repetition of topics. However, both can reduce repetition; but presence_penalty is often preferred for reducing repetition of content. Based on typical usage, presence_penalty is the better choice.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Scenario analysis trap
Frequency penalty reduces repetition of frequent tokens, but presence penalty is more effective for this scenario.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase presencePenalty to 0.5 — Option D is correct because presence_penalty reduces the likelihood of repeating tokens that have already appeared. Option A is wrong because temperature controls creativity. Option B is wrong because topP controls nucleus sampling. Option C is wrong because frequency_penalty is set to 0 and could be increased, but presence_penalty is more effective for repetition of topics. However, both can reduce repetition; but presence_penalty is often preferred for reducing repetition of content. Based on typical usage, presence_penalty is the better choice.
What should I do if I get this AI-102 question wrong?
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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