- A
Predictive policing AI is too expensive to implement at city scale
Why wrong: Cost is not the primary ethical concern — the ethical issues center on fairness, bias, and civil rights.
- B
Potential to perpetuate racial bias, undermine due process, and create discriminatory self-fulfilling prophecies
Models trained on historically biased policing data target minority communities more, creating self-fulfilling bias cycles that undermine civil rights.
- C
Predictive policing models are too slow to be useful for real-time decisions
Why wrong: Performance speed is a technical concern — the ethical issues are bias, fairness, and civil liberties.
- D
Predictive policing AI might predict crimes in the wrong ZIP code
Why wrong: Geographic accuracy is a technical performance concern — the core ethical issues are systemic bias, discrimination, and civil rights.
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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
What is the ethical concern with using AI for 'predictive policing'?
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
Potential to perpetuate racial bias, undermine due process, and create discriminatory self-fulfilling prophecies
Option B is correct because predictive policing AI systems often rely on historical crime data, which can contain inherent biases from over-policing in minority communities. This can lead to a feedback loop where the AI predicts more crime in those areas, prompting more police presence, which in turn generates more arrests and reinforces the original bias. Such systems also risk undermining due process by making decisions based on statistical correlations rather than individual evidence, and can create self-fulfilling prophecies where predicted crime hotspots become actual crime hotspots due to increased enforcement.
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.
- ✗
Predictive policing AI is too expensive to implement at city scale
Why it's wrong here
Cost is not the primary ethical concern — the ethical issues center on fairness, bias, and civil rights.
- ✓
Potential to perpetuate racial bias, undermine due process, and create discriminatory self-fulfilling prophecies
Why this is correct
Models trained on historically biased policing data target minority communities more, creating self-fulfilling bias cycles that undermine civil rights.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Predictive policing models are too slow to be useful for real-time decisions
Why it's wrong here
Performance speed is a technical concern — the ethical issues are bias, fairness, and civil liberties.
- ✗
Predictive policing AI might predict crimes in the wrong ZIP code
Why it's wrong here
Geographic accuracy is a technical performance concern — the core ethical issues are systemic bias, discrimination, and civil rights.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may focus on practical limitations like cost or accuracy (options A, C, D) rather than recognizing that the core ethical concern in AI-900 is always about fairness, bias, and societal impact, not technical performance.
Detailed technical explanation
How to think about this question
Under the hood, predictive policing models often use machine learning algorithms like random forests or neural networks trained on historical arrest records, calls for service, and socioeconomic data. A subtle behavior is that these models can amplify 'label bias'—where the training labels (arrests) reflect police activity rather than actual crime rates—leading to a feedback loop that disproportionately targets marginalized neighborhoods. In a real-world scenario, the PredPol system used by several U.S. police departments was found to direct officers to low-income and minority areas more frequently, even when controlling for actual crime incidence, demonstrating how algorithmic bias can entrench systemic inequality.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Describe Artificial Intelligence workloads and considerations — study guide chapter
Learn the concepts, then practise the questions
- →
Describe Artificial Intelligence workloads and considerations practice questions
Targeted practice on this topic area only
- →
All AI-900 questions
1,020 questions across all exam domains
- →
Microsoft Azure AI Fundamentals AI-900 study guide
Full concept coverage aligned to exam objectives
- →
AI-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe Artificial Intelligence workloads and considerations practice questions
Practise AI-900 questions linked to Describe Artificial Intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure practice questions
Practise AI-900 questions linked to Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of computer vision workloads on Azure.
Describe features of Natural Language Processing workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of Natural Language Processing workloads on Azure.
Describe features of generative AI workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of generative AI workloads on Azure.
AI-900 fundamentals practice questions
Practise AI-900 questions linked to AI-900 fundamentals.
AI-900 scenario practice questions
Practise AI-900 questions linked to AI-900 scenario.
AI-900 troubleshooting practice questions
Practise AI-900 questions linked to AI-900 troubleshooting.
Practice this exam
Start a free AI-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this AI-900 question test?
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: Potential to perpetuate racial bias, undermine due process, and create discriminatory self-fulfilling prophecies — Option B is correct because predictive policing AI systems often rely on historical crime data, which can contain inherent biases from over-policing in minority communities. This can lead to a feedback loop where the AI predicts more crime in those areas, prompting more police presence, which in turn generates more arrests and reinforces the original bias. Such systems also risk undermining due process by making decisions based on statistical correlations rather than individual evidence, and can create self-fulfilling prophecies where predicted crime hotspots become actual crime hotspots due to increased enforcement.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 11, 2026
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.