AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A company develops an AI system to screen job resumes and rank candidates for interviews. The system is trained on historical hiring data that favored candidates from certain well-known universities. The company decides to deploy the system without any adjustments to address this bias. Which Microsoft responsible AI principle is most directly being violated?
⚠ Common exam trap
Candidates often confuse 'fairness' with 'inclusiveness' because both relate to ethical AI, but inclusiveness is about designing for diverse user groups (e.g., accessibility), while fairness specifically addresses bias and discrimination in model outcomes.
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
✓
Fairness
(Fairness) because the AI system was trained on biased historical data that favored candidates from certain universities, and deploying it without adjustments directly violates the fairness principle. Fairness in responsible AI requires that systems treat all people equitably and do not discriminate based on protected attributes such as educational background. By not mitigating the bias, the system perpetuates historical inequities in the hiring process.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Fairness
Why this is correct
Fairness is the AI principle that directly addresses the scenario: the resume-screening model's outcomes must not systematically disadvantage individuals based on protected attributes (e.g., gender, ethnicity, age). A model trained on biased historical hiring data can learn and perpetuate discriminatory patterns, producing unfair rankings even without explicit demographic inputs. Correcting this requires evaluating the system with fairness metrics (e.g., demographic parity or equalized odds) and mitigating bias in training data or model outputs before deployment.
- ✗
Inclusiveness
Why it's wrong here
Inclusiveness emphasizes designing AI systems to be usable and accessible by people of all abilities, backgrounds, and contexts, such as meeting accessibility standards for users with disabilities. While an unfair resume screener may disproportionately exclude certain groups, that is a consequence of bias, not a lack of inclusive design; the system might still be fully accessible to all users. Thus, inclusiveness alone does not capture the core violation of producing unequal outcomes—that is a fairness deficiency.
When this WOULD be correct
A question asks: 'A company builds a chatbot that only supports English, excluding non-English speakers. Which principle is violated?' In that scenario, Inclusiveness is correct because the system fails to accommodate diverse users.
- ✗
Reliability and Safety
Why it's wrong here
Reliability and Safety concern whether an AI system performs consistently under expected conditions, remains available, and avoids causing physical or financial harm through errors or failures—not the distribution of outcomes across demographic groups. For a resume-ranking tool, reliability might mean stable ranking behavior with new inputs, but a biased model can still be 'reliable' in the sense that it consistently ranks candidates from certain groups lower. Therefore, the problem here is not a reliability failure but an ethical and legal failure in equitable treatment.
When this WOULD be correct
A question where an AI system for medical diagnosis produces inconsistent results across different patient groups due to sensor noise or data quality issues, leading to potential harm. The correct principle would be Reliability and Safety.
- ✗
Privacy and Security
Why it's wrong here
Privacy and Security focus on protecting candidates' personal data from unauthorized access, misuse, or leakage, and ensuring the system is resilient to attacks or data breaches. In the described scenario, there is no evidence that resume data is being mishandled or that the AI system's security is compromised; the issue is that the model's ranking decisions are biased. Hence, privacy and security are irrelevant to this specific violation.
When this WOULD be correct
This option would be correct in a scenario where an AI system exposes sensitive candidate information (e.g., social security numbers, contact details) without proper encryption or access controls, violating data protection regulations.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓FairnessCorrect answer▾
Why this is correct
Fairness is the AI principle that directly addresses the scenario: the resume-screening model's outcomes must not systematically disadvantage individuals based on protected attributes (e.g., gender, ethnicity, age). A model trained on biased historical hiring data can learn and perpetuate discriminatory patterns, producing unfair rankings even without explicit demographic inputs. Correcting this requires evaluating the system with fairness metrics (e.g., demographic parity or equalized odds) and mitigating bias in training data or model outputs before deployment.
✗InclusivenessWrong answer — click to see why▾
Why this is wrong here
The question focuses on bias in hiring decisions based on university preference, which directly relates to Fairness. Inclusiveness is about designing for diverse user needs, not about correcting biased outcomes in a system's decisions.
★ When this WOULD be the correct answer
A question asks: 'A company builds a chatbot that only supports English, excluding non-English speakers. Which principle is violated?' In that scenario, Inclusiveness is correct because the system fails to accommodate diverse users.
Why candidates choose this
Candidates may confuse 'inclusiveness' with 'fairness' because both address diversity, but inclusiveness is about accessibility and representation, not about correcting biased decision-making.
✗Reliability and SafetyWrong answer — click to see why▾
Why this is wrong here
The question focuses on bias in hiring decisions, which directly relates to Fairness. Reliability and Safety concerns system failures or errors, not biased outcomes from training data.
★ When this WOULD be the correct answer
A question where an AI system for medical diagnosis produces inconsistent results across different patient groups due to sensor noise or data quality issues, leading to potential harm. The correct principle would be Reliability and Safety.
Why candidates choose this
Candidates may confuse bias (unfair outcomes) with unreliability (inconsistent performance), thinking that biased results are a form of system unreliability.
✗Privacy and SecurityWrong answer — click to see why▾
Why this is wrong here
The question focuses on bias in hiring decisions due to historical data favoring certain universities, which directly relates to fairness, not privacy or security. Privacy and security concerns involve protecting personal data from unauthorized access or misuse, which is not the issue here.
★ When this WOULD be the correct answer
This option would be correct in a scenario where an AI system exposes sensitive candidate information (e.g., social security numbers, contact details) without proper encryption or access controls, violating data protection regulations.
Why candidates choose this
Candidates may confuse bias with data privacy issues, thinking that the historical data's bias stems from mishandling personal information, or they may broadly associate any ethical concern with privacy without analyzing the specific violation.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Responsible AI Principles
Key term
Bias
Bias in AI is a systematic error in data or algorithms that leads to unfair or inaccurate outcomes, often reflecting real-world prejudices.
Key term
Responsible AI
A framework of ethical principles and practices that ensure artificial intelligence systems are developed and deployed in a transparent, fair, accountable, and safe manner.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 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-900 exam.