The correct adjustment is to decrease the temperature parameter. Lowering temperature reduces the randomness of the model’s token selection, forcing it to choose the most probable next words rather than exploring less likely, creative alternatives—this directly curbs the generation of false claims by making outputs more deterministic and grounded in the training data’s highest-confidence patterns. On the Microsoft Azure AI Engineer Associate AI-102 exam, this concept tests your understanding of how inference parameters control model behavior without retraining; a common trap is confusing temperature with top-p or frequency penalty, which affect diversity differently. Remember the memory tip: “Cool it down to keep it factual”—lower temperature chills the model’s creativity, making it stick to safer, more accurate predictions.
AI-102 Implement generative AI solutions Practice Question
This AI-102 practice question tests your understanding of implement generative ai solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
A company uses Azure OpenAI to generate product descriptions. They notice that the model occasionally produces descriptions that include false claims about product features. The company needs to reduce the frequency of these inaccuracies without changing the training data. Which parameter adjustment would be most effective?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Decrease the temperature parameter
Decreasing the temperature parameter reduces the randomness of the model's output, making it more deterministic and less likely to generate creative but factually incorrect statements. This directly addresses the need to reduce false claims without modifying training data, as lower temperature forces the model to rely on its most probable (and typically more accurate) token predictions.
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 the top_p parameter
Why it's wrong here
Increasing top_p makes outputs more diverse, potentially increasing inaccuracies.
✗
Increase the max_tokens parameter
Why it's wrong here
Increasing max_tokens allows longer outputs but does not reduce false claims.
✓
Decrease the temperature parameter
Why this is correct
Lower temperature makes the model more focused and less likely to hallucinate.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the frequency_penalty parameter
Why it's wrong here
Frequency penalty reduces word repetition, not factual accuracy.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse temperature with creativity or length control, assuming that increasing randomness (higher temperature) or extending output length (max_tokens) will somehow improve accuracy, when in fact lower temperature is the standard parameter for reducing hallucinations.
Trap categories for this question
Command / output trap
Increasing top_p makes outputs more diverse, potentially increasing inaccuracies.
Detailed technical explanation
How to think about this question
Temperature controls the softmax distribution's sharpness: lower values (e.g., 0.1) concentrate probability mass on the highest-likelihood tokens, while higher values (e.g., 1.0) flatten the distribution, increasing randomness. In Azure OpenAI, temperature ranges from 0 to 2, and for tasks requiring factual consistency (e.g., product descriptions), values below 0.3 are commonly used to minimize hallucinations. A subtle behavior is that temperature interacts with top_p—if both are adjusted, top_p can override temperature effects, so typically only one is tuned.
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: Decrease the temperature parameter — Decreasing the temperature parameter reduces the randomness of the model's output, making it more deterministic and less likely to generate creative but factually incorrect statements. This directly addresses the need to reduce false claims without modifying training data, as lower temperature forces the model to rely on its most probable (and typically more accurate) token predictions.
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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