Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A financial services company uses an AI system to detect fraudulent credit card transactions. After deployment, the system incorrectly flags a significant number of legitimate transactions as fraudulent, causing customer dissatisfaction. The company wants to reduce these false positives while still catching most fraudulent transactions. Which Microsoft responsible AI principle should guide their redesign of the system?
⚠ Common exam trap
Candidates often confuse 'false positives causing customer dissatisfaction' with a fairness or transparency issue, when in fact it is a reliability and safety problem about the system's accuracy and trustworthiness in production.
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
✓
Reliability and safety
The Reliability and safety principle emphasizes that AI systems should perform reliably, safely, and consistently under normal conditions. In this scenario, the high rate of false positives indicates the system is not operating reliably for legitimate transactions, causing customer harm. Redesigning to reduce false positives while maintaining fraud detection aligns directly with improving the system's reliability and safety for end users.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Reliability and safety
Why this is correct
In fraud detection, reliability hinges on tuning the decision threshold to balance false positives and false negatives—too many false positives block legitimate transactions, while false negatives let fraudulent activity slip through. Safety extends this by demanding robust performance under real-world data drift and graceful handling of edge cases, so the system's errors remain bounded and non-harmful. This principle directly governs prediction accuracy and error trade-offs.
- ✗
Fairness
Why it's wrong here
Fairness would instead scrutinize whether the fraud model's error rates differ systematically across protected demographic groups, such as age, gender, or ethnicity. The concern is disparate impact—for example, if legitimate transactions from one group are flagged more often than from another—not the overall balance of false positives and false negatives across the entire population. Since the question focuses on global error trade-offs rather than subgroup bias, this principle is misapplied.
- ✗
Transparency
Why it's wrong here
Transparency addresses whether the AI system's decision-making process can be understood and explained to stakeholders, regulators, and affected customers. It involves techniques like feature-attribution explanations, audit trails, and clear documentation, not the minimization of false positives or false negatives. A model can be perfectly transparent yet perform poorly, so reducing prediction errors is not what this principle covers.
- ✗
Privacy and security
Why it's wrong here
Privacy and security govern the protection of training data and live transaction records from unauthorized access, leakage, or malicious extraction through techniques such as encryption, access controls, and differential privacy. They do not address the accuracy of individual predictions or the trade-off between false positives and false negatives. While critical for compliance, these controls are orthogonal to the reliability concerns implied by error balancing.
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Responsible AI Principles
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.
Key term
Reliability and safety
Reliability and safety in IT means that systems consistently perform their intended functions without failure and that they operate without causing harm to people, data, or the environment.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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