AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
What is 'credit scoring' as an AI workload and what responsible AI concerns does it raise?
⚠ Common exam trap
It's easy for candidates to confuse 'credit scoring' with simple monitoring or non-AI scoring systems, but the exam specifically tests the understanding that it is a predictive ML workload with ethical implications around fairness, bias, and explainability.
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
✓
ML for predicting loan repayment risk — with fairness, bias, and explainability concerns
Credit scoring in AI refers to machine learning models that predict the likelihood of a borrower repaying a loan. This raises responsible AI concerns around fairness (e.g., models may discriminate against protected groups), bias (e.g., training data may reflect historical inequalities), and explainability (e.g., complex models like gradient-boosted trees are often black boxes, making it hard to justify decisions to regulators or customers).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A system for automatically assigning credit scores to software bugs in a development backlog
Why it's wrong here
This option misuses the term 'credit scoring' in a software engineering context. A bug prioritization system might rank bugs by severity, impact, or urgency using heuristic rules or even ML, but that is not the same as financial credit scoring. Credit scoring in the AI-900 context refers specifically to predicting a consumer's likelihood of repaying a loan, which has life-altering consequences and is regulated by laws like the Equal Credit Opportunity Act and GDPR's right to explanation. A bug-triaging tool has none of those fairness, bias, or explainability implications.
- ✓
ML for predicting loan repayment risk — with fairness, bias, and explainability concerns
Why this is correct
This is the classic, high-stakes example of credit scoring in machine learning. A supervised model is trained on historical loan repayment data to predict the probability of default for new applicants, using features such as income, credit history, and debt-to-income ratio. Fairness is a major concern because biased historical data can lead to disparate impact across demographic groups, and regulations like GDPR Article 22 require that applicants receive explanations for automated credit decisions. This combination of predictive power, ethical risk, and regulatory scrutiny makes it the correct answer.
- ✗
Monitoring whether a customer has used all their credit within an approved limit
Why it's wrong here
Monitoring credit utilization is a real-time, rule-based banking operation, not predictive credit scoring. It compares the customer's current balance to their approved limit and triggers alerts or blocks transactions when the limit is reached. This is descriptive monitoring rather than forward-looking risk prediction, and it does not involve training a model on historical data to estimate repayment probability. As a result, it lacks the fairness, bias, and explainability challenges that make credit scoring a key responsible AI topic.
- ✗
An internal system for scoring the quality of AI projects within an organisation
Why it's wrong here
Scoring the quality of AI projects is an internal governance or portfolio-management task, not consumer credit scoring. It might use a weighted scorecard to rank projects by ROI, risk, or alignment with strategy, but the scores are only for internal decision-making and do not affect individual rights. In the AI-900 context, credit scoring is specifically about using ML to assess a borrower's financial risk, which carries substantial ethical and regulatory obligations. Internal project scoring is irrelevant to fairness, bias, and explainability concerns related to consumer lending.
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Related to this question
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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
Training data
Training data is a set of examples used to teach a machine learning model how to make predictions or decisions.
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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.