- A
AI systems with publicly available source code that anyone can inspect
Why wrong: Open-source AI is about code transparency — XAI is about explaining specific decisions to affected parties, not publishing source code.
- B
AI systems that can explain their decisions in understandable terms — required for regulatory compliance
XAI enables explanation of individual decisions — required by GDPR, EU AI Act, and sector regulations for consequential automated decisions.
- C
AI models that are simple enough for non-experts to rebuild from scratch
Why wrong: Model simplicity is one approach — XAI is the broader field of making any model's decisions interpretable regardless of complexity.
- D
AI systems that automatically explain errors in user-submitted code
Why wrong: Code explanation is a developer tool — explainable AI refers to systems that explain their own decision-making to affected stakeholders.
Quick Answer
The answer is explainable AI (XAI), which refers to AI systems that can explain their decisions in understandable terms, and it is required in regulated industries primarily for regulatory compliance. This is because regulations like the GDPR’s ‘right to explanation’ and the EU AI Act mandate that automated decisions in sectors such as finance, healthcare, and insurance must be transparent and auditable, meaning a black-box model that cannot justify its outputs would violate the law. On the Microsoft Azure AI-900 exam, this concept tests your understanding of responsible AI principles, often appearing in questions about fairness, accountability, and transparency. A common trap is confusing explainability with interpretability—while both relate to understanding, explainability specifically focuses on providing post-hoc justifications for a model’s output, not just its internal logic. Remember the mnemonic “XAI = eXplainable AI for eXternal compliance” to keep the regulatory driver front of mind.
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 'explainable AI' (XAI) and why is it required in regulated industries?
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
AI systems that can explain their decisions in understandable terms — required for regulatory compliance
Explainable AI (XAI) refers to AI systems that provide human-understandable justifications for their decisions, predictions, or recommendations. In regulated industries such as finance, healthcare, and insurance, regulations like GDPR's 'right to explanation' and the EU AI Act require that automated decisions be transparent and auditable, making XAI a compliance necessity.
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.
- ✗
AI systems with publicly available source code that anyone can inspect
Why it's wrong here
Open-source AI is about code transparency — XAI is about explaining specific decisions to affected parties, not publishing source code.
- ✓
AI systems that can explain their decisions in understandable terms — required for regulatory compliance
Why this is correct
XAI enables explanation of individual decisions — required by GDPR, EU AI Act, and sector regulations for consequential automated decisions.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
AI models that are simple enough for non-experts to rebuild from scratch
Why it's wrong here
Model simplicity is one approach — XAI is the broader field of making any model's decisions interpretable regardless of complexity.
- ✗
AI systems that automatically explain errors in user-submitted code
Why it's wrong here
Code explanation is a developer tool — explainable AI refers to systems that explain their own decision-making to affected stakeholders.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing 'explainable AI' with general transparency concepts like open-source code or model simplicity, when the exam specifically tests that XAI is about producing human-readable justifications for regulatory compliance.
Detailed technical explanation
How to think about this question
XAI techniques include LIME (Local Interpretable Model-agnostic Explanations), which approximates a complex model locally with a simpler interpretable model, and SHAP (SHapley Additive exPlanations), which uses game-theoretic Shapley values to assign feature importance. In a real-world scenario, a bank using a credit-scoring AI must provide a reason for denying a loan (e.g., 'high debt-to-income ratio') to comply with fair lending laws; XAI tools generate these feature-level attributions.
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
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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: AI systems that can explain their decisions in understandable terms — required for regulatory compliance — Explainable AI (XAI) refers to AI systems that provide human-understandable justifications for their decisions, predictions, or recommendations. In regulated industries such as finance, healthcare, and insurance, regulations like GDPR's 'right to explanation' and the EU AI Act require that automated decisions be transparent and auditable, making XAI a compliance necessity.
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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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.
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