AI-900 Practice Question: Describe features of generative AI workloads on Azure
This AI-900 practice question tests your understanding of describe features of generative ai workloads on azure. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. A key principle to apply: frequency_penalty reduces the likelihood of repeating tokens already in the generated text.. 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 developer uses Azure OpenAI Service to generate product reviews for an e-commerce site. The developer notices that the model often repeats the same phrases within the same review, making the output sound unnatural. Which parameter should the developer adjust to reduce this repetition?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Frequency_penalty
The frequency_penalty parameter reduces the likelihood of the model repeating the same phrases by penalizing tokens that have already appeared in the generated text. A higher frequency_penalty value (e.g., 0.5 to 1.0) discourages the model from reusing the same words or phrases, making the output more diverse and natural. This directly addresses the issue of repetitive phrasing in product reviews.
Key principle: Frequency_penalty reduces the likelihood of repeating tokens already in the generated 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.
✗
Temperature
Why it's wrong here
Temperature influences randomness in token selection, not specifically repetition.
✗
Top_p
Why it's wrong here
Top_p (nucleus sampling) controls the cumulative probability threshold for token selection, but does not directly penalize repetition.
✗
Max_tokens
Why it's wrong here
Max_tokens sets the maximum length of the output; it does not affect repetition within the generated text.
✓
Frequency_penalty
Why this is correct
Frequency_penalty reduces the likelihood of repeating tokens that have already been used, directly addressing the repetition issue.
Related concept
Frequency_penalty reduces the likelihood of repeating tokens already in the generated text.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse frequency_penalty with temperature or top_p, assuming any parameter that affects output diversity will solve repetition, but only frequency_penalty directly penalizes repeated tokens.
Trap categories for this question
Command / output trap
Max_tokens sets the maximum length of the output; it does not affect repetition within the generated text.
Detailed technical explanation
How to think about this question
Under the hood, frequency_penalty works by subtracting a penalty value from the logit (pre-softmax score) of each token each time it appears in the generated sequence, effectively reducing its probability of being selected again. This is distinct from presence_penalty, which penalizes tokens based on whether they have appeared at all, not how often. In real-world scenarios, adjusting frequency_penalty is critical for tasks like summarization or review generation where natural variation is required, while temperature and top_p are better suited for controlling creativity and focus.
KKey Concepts to Remember
Frequency_penalty reduces the likelihood of repeating tokens already in the generated text.
It helps generate more diverse and natural-sounding language.
A higher frequency_penalty value discourages repetition more strongly.
It is distinct from temperature, top_p, and max_tokens in its function.
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
Frequency_penalty reduces the likelihood of repeating tokens already in the generated text.
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. Frequency_penalty reduces the likelihood of repeating tokens already in the generated text. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Review frequency_penalty reduces the likelihood of repeating tokens already in the generated text., then practise related AI-900 questions on the same topic to reinforce the concept.
Describe features of generative AI workloads on Azure — This question tests Describe features of generative AI workloads on Azure — Frequency_penalty reduces the likelihood of repeating tokens already in the generated text..
What is the correct answer to this question?
The correct answer is: Frequency_penalty — The frequency_penalty parameter reduces the likelihood of the model repeating the same phrases by penalizing tokens that have already appeared in the generated text. A higher frequency_penalty value (e.g., 0.5 to 1.0) discourages the model from reusing the same words or phrases, making the output more diverse and natural. This directly addresses the issue of repetitive phrasing in product reviews.
What should I do if I get this AI-900 question wrong?
Review frequency_penalty reduces the likelihood of repeating tokens already in the generated text., then practise related AI-900 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Frequency_penalty reduces the likelihood of repeating tokens already in the generated text.
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