AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
This AI-900 practice question tests your understanding of describe artificial intelligence workloads and considerations. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 is about making AI systems understandable and explaining decisions, not directly about unequal accuracy across groups.
✗
Accountability
Why it's wrong here
Accountability refers to who is responsible for the AI system's outcomes, not the performance disparity itself.
✓
Fairness
Why this is correct
Fairness ensures AI systems do not discriminate or produce biased outcomes, directly addressing the accuracy imbalance in different neighborhoods.
Related concept
Read the scenario before looking for a memorised answer.
✗
Privacy and security
Why it's wrong here
Privacy and security focus on protecting data from unauthorized access, not on model performance across subgroups.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
Fairness in AI often involves measuring metrics like demographic parity or equalized odds across groups. In practice, sparse data for a subgroup can lead to higher variance in predictions, which may be addressed by techniques such as stratified sampling during training or using fairness-aware algorithms that penalize disparate impact. For example, in traffic prediction, a model trained predominantly on dense urban data may fail to generalize to low-income areas where traffic patterns differ, requiring either synthetic data generation or domain adaptation methods.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe Artificial Intelligence workloads and considerations — This question tests Describe Artificial Intelligence workloads and considerations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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