Fairness — Microsoft Azure AI Fundamentals (AI-900) Practice Questions
Fairness is one of the six core Microsoft Responsible AI principles and is a focused testable concept on AI-900, referring to the requirement that AI systems treat all individuals and groups equitably without producing discriminatory outcomes. The exam explores how bias can be introduced through unrepresentative training data, biased labels, or features that serve as proxies for protected characteristics such as race or gender. Candidates should know that Microsoft provides tools like Fairlearn to measure and mitigate unfairness in machine learning models. AI-900 questions on fairness often involve a scenario where an AI system performs differently across demographic groups and ask candidates to identify the issue or the appropriate response.
Free questions on fairness
More fairness questions in the full bank
- Which component is essential for implementing responsible AI in Azure ML? Unlock answer & explanation →
- What is the purpose of fairness assessment in Azure ML? Unlock answer & explanation →
- In responsible AI, why is it important to address data bias before training a machine learning model? Unlock answer & explanation →