Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A city government implements an AI system to analyze traffic camera feeds and predict congestion. The system is found to be less accurate for neighborhoods with lower-income populations because historical traffic data from those areas is sparse. Which Microsoft responsible AI principle is most directly relevant to address this issue?
⚠ Common exam trap
A common mix-up: candidates confuse fairness with transparency, assuming that explaining why the model is inaccurate solves the underlying performance disparity, when in fact fairness requires actively correcting the imbalance.
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
The system's reduced accuracy for lower-income neighborhoods due to sparse historical data is a direct fairness issue. Fairness in AI requires that systems perform equitably across different demographic groups, and this scenario describes a clear disparity in model performance based on socioeconomic factors. Addressing this would involve techniques like data augmentation, reweighting, or collecting more representative data to mitigate bias.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Transparency
Why it's wrong here
Transparency aims to make AI decisions interpretable and explainable to stakeholders, such as providing clear justifications for why a traffic light timing is adjusted. However, a fully transparent system could still exhibit unequal accuracy across neighborhoods; transparency alone does not prevent or measure performance disparities. The core issue here is the imbalance in prediction quality, which transparency does not directly tackle.
- ✗
Accountability
Why it's wrong here
Accountability deals with assigning responsibility for AI outcomes and establishing governance frameworks for auditing and remediation. It is a process-level concern about who is responsible, not a property of the model's performance itself. The fact that accuracy differs among neighborhoods is a substantive fairness flaw, not merely a question of accountability.
- ✓
Fairness
Why this is correct
Fairness directly targets whether AI systems produce unbiased, equitable outcomes across all population groups. In this scenario, unequal accuracy in different neighborhoods indicates that the model may be under-representing certain areas in training data or using features that disadvantage them. Fairness ensures that the system does not systematically disadvantage any group, making it the correct principle to address this disparity.
- ✗
Privacy and security
Why it's wrong here
Privacy and security concerns safeguarding data from unauthorized access, ensuring encryption, access controls, and compliance with regulations like GDPR. It does not address the issue of model accuracy varying across neighborhoods, which is about algorithmic bias. Even with perfect data protection, the AI system could still produce inaccurate results for certain groups, so this option is incorrect.
Go deeper
Related to this question
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Responsible AI Principles
Key term
Fairness
Fairness in AI means designing and deploying machine learning models that do not produce biased outcomes against any group of people based on protected characteristics like race, gender, or age.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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JA
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.