AI-300 Generative AI Optimization Practice Question
You notice your model is outputting redundant information. Which parameter specifically targets the penalty for repeating tokens?
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
✓
frequency_penalty
The frequency_penalty parameter is explicitly designed to reduce the probability of tokens that have already appeared.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
max_tokens
Why it's wrong here
Limits length, does not control repetition.
- ✗
presence_penalty
Why it's wrong here
Presence penalty penalizes tokens that have appeared at least once.
- ✓
frequency_penalty
Why this is correct
Frequency penalty penalizes tokens based on how many times they have already appeared.
- ✗
top_p
Why it's wrong here
Controls the probability mass for nucleus sampling.
About these practice questions
This AI-300 question is part of Courseiva's 204-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
This AI-300 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-300 exam.