Question 787 of 1,020

Describe Artificial Intelligence workloads and considerations →easyMultiple ChoiceObjective-mapped

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 'credit scoring' as an AI workload and what responsible AI concerns does it raise?

A
A system for automatically assigning credit scores to software bugs in a development backlog
Why wrong: Bug prioritisation systems are developer tools — credit scoring predicts financial creditworthiness, with significant fairness implications.
B
ML for predicting loan repayment risk — with fairness, bias, and explainability concerns
Credit scoring has life-altering consequences — historical bias, demographic proxies, and GDPR explanation rights require careful responsible AI practices.
C
Monitoring whether a customer has used all their credit within an approved limit
Why wrong: Credit utilisation monitoring is banking operations — credit scoring predicts future repayment risk from features.
D
An internal system for scoring the quality of AI projects within an organisation
Why wrong: AI project quality scoring is internal governance — credit scoring is consumer lending risk prediction with significant ethical implications.

Full breakdown with real-world context →

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).

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.

✗
A system for automatically assigning credit scores to software bugs in a development backlog
Why it's wrong here
Bug prioritisation systems are developer tools — credit scoring predicts financial creditworthiness, with significant fairness implications.
✓
ML for predicting loan repayment risk — with fairness, bias, and explainability concerns
Why this is correct
Credit scoring has life-altering consequences — historical bias, demographic proxies, and GDPR explanation rights require careful responsible AI practices.
Related concept
Read the scenario before looking for a memorised answer.
✗
Monitoring whether a customer has used all their credit within an approved limit
Why it's wrong here
Credit utilisation monitoring is banking operations — credit scoring predicts future repayment risk from features.
✗
An internal system for scoring the quality of AI projects within an organisation
Why it's wrong here
AI project quality scoring is internal governance — credit scoring is consumer lending risk prediction with significant ethical implications.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates 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.

Detailed technical explanation

How to think about this question

Under the hood, credit scoring models often use logistic regression, decision trees, or ensemble methods (e.g., XGBoost) trained on features like income, debt-to-income ratio, and payment history. A subtle behavior is that these models can inadvertently encode proxy variables (e.g., zip code as a proxy for race), leading to disparate impact under the Equal Credit Opportunity Act (ECOA). In a real-world scenario, a bank using a black-box neural network for credit scoring might fail to provide an adverse action notice with specific reasons for denial, violating Regulation B.

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.

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.

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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: 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).

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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