AI-103 Implement Generative AI And Agentic Solutions Practice Question
When designing a Responsible AI evaluation workflow in Azure AI Foundry, which TWO metrics are specifically used to assess content safety and potential harms? (Choose two.)
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
✓
Hate speech and unfairness detection
Violence, sexual, hate, and self-harm detection along with protected material detection are core safety metrics in Azure AI Foundry.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Hate speech and unfairness detection
Why this is correct
Hate speech and unfairness metrics assess content safety and bias.
- ✗
Word count frequency
Why it's wrong here
Word count is a basic descriptive statistic, not a safety metric.
- ✗
BLEU machine translation score
Why it's wrong here
BLEU measures translation similarity, not safety or harm.
- ✓
Sexual content detection
Why this is correct
Sexual content detection evaluates safety compliance.
- ✗
Model parameter count
Why it's wrong here
Parameter count describes model size, not safety performance.
About these practice questions
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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-103 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-103 exam.