Question 646 of 1,020

Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped

What Is Explainable AI (XAI)? Techniques for Transparent AI

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 important for responsible AI?

A
AI systems that can explain jokes and riddles to users
Why wrong: XAI is about explaining model decisions — not joke comprehension.
B
Techniques that make AI decision-making understandable to humans, supporting transparency and trust
XAI explains how and why AI makes specific decisions, enabling bias detection, regulatory compliance, and user trust.
C
AI systems designed to teach other AI systems
Why wrong: Teaching AI is meta-learning — XAI is about making AI decisions interpretable to humans.
D
AI that automatically generates explanations of its errors
Why wrong: Error explanation is one aspect of XAI — the broader goal is making AI decision-making processes generally interpretable.

Full breakdown with real-world context →

Quick Answer

The correct answer is that explainable AI (XAI) refers to a set of techniques and methods that make AI decision-making understandable to humans, directly supporting transparency and trust. This is correct because XAI provides human-interpretable explanations for model outputs, which is essential for verifying that an AI system behaves as intended, identifying hidden biases, and ensuring accountability. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of responsible AI principles, often appearing in questions about fairness, reliability, and the "right to explanation" under regulations like GDPR. A common trap is confusing XAI with simply describing what a model does—remember, XAI focuses on explaining *why* a specific decision was made, not just listing features. Memory tip: think of XAI as the "why behind the what" for building user trust.

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

✓

Techniques that make AI decision-making understandable to humans, supporting transparency and trust

Explainable AI (XAI) refers to a set of techniques and methods that produce human-understandable explanations of AI model decisions, outputs, and behaviors. It is critical for responsible AI because it enables transparency, builds user trust, supports regulatory compliance (e.g., GDPR's right to explanation), and helps identify and mitigate bias or errors in model predictions.

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 that can explain jokes and riddles to users
Why it's wrong here
XAI is about explaining model decisions — not joke comprehension.
✓
Techniques that make AI decision-making understandable to humans, supporting transparency and trust
Why this is correct
XAI explains how and why AI makes specific decisions, enabling bias detection, regulatory compliance, and user trust.
Related concept
Read the scenario before looking for a memorised answer.
✗
AI systems designed to teach other AI systems
Why it's wrong here
Teaching AI is meta-learning — XAI is about making AI decisions interpretable to humans.
✗
AI that automatically generates explanations of its errors
Why it's wrong here
Error explanation is one aspect of XAI — the broader goal is making AI decision-making processes generally interpretable.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates confuse 'explainable AI' with 'AI that can explain itself in natural language' (Option A) or with 'error-handling AI' (Option D), when in fact XAI is a broad set of interpretability techniques focused on transparency and trust, not conversational ability or post-hoc error reporting.

Detailed technical explanation

How to think about this question

Under the hood, XAI techniques include LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), which approximate model behavior locally using simpler surrogate models or game-theoretic feature contributions. In a real-world scenario, a bank using a deep learning model for loan approval must use XAI to show an applicant which features (e.g., income, credit history) most influenced the rejection, ensuring fairness and auditability under regulations like the Equal Credit Opportunity Act.

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.

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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: Techniques that make AI decision-making understandable to humans, supporting transparency and trust — Explainable AI (XAI) refers to a set of techniques and methods that produce human-understandable explanations of AI model decisions, outputs, and behaviors. It is critical for responsible AI because it enables transparency, builds user trust, supports regulatory compliance (e.g., GDPR's right to explanation), and helps identify and mitigate bias or errors in model predictions.

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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