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
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.
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
The trap here is that 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.
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
Under the hood, presencePenalty adds a fixed negative bias to the logit of any token that has already been generated in the current sequence, regardless of how many times it has appeared. This is distinct from frequencyPenalty, which applies a penalty proportional to the token's cumulative frequency. In practice, for reducing short-term repetition (e.g., repeating the same phrase in consecutive sentences), presencePenalty is more effective, while frequencyPenalty is better for reducing overuse of common words across longer outputs.
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.
TExam Day Tips
→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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 — 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.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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