Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
What is 'explainable AI' (XAI) and why is it important for responsible AI?
⚠ Common exam trap
Test-takers frequently 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.
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.
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
Understanding humor in jokes and riddles requires commonsense reasoning, cultural knowledge, and language pragmatics, not the interpretability of model predictions. XAI is concerned with explaining why a model produced a specific decision, such as a loan denial or medical diagnosis, not with generating entertaining or human-like explanations of jokes.
- ✓
Techniques that make AI decision-making understandable to humans, supporting transparency and trust
Why this is correct
Explainable AI (XAI) encompasses techniques such as feature-attribution methods (e.g., SHAP, LIME), saliency maps, and surrogate models that translate a model's internal logic into human-comprehensible rationale. These techniques reveal which input features most influenced a particular prediction, enabling stakeholders to detect bias, validate fairness, and satisfy regulatory requirements while building user trust.
- ✗
AI systems designed to teach other AI systems
Why it's wrong here
This describes meta-learning or knowledge distillation, where one model's outputs are used to train or improve another model. XAI is not machine-to-machine teaching; it is fundamentally human-centered because it renders a model's reasoning legible to people so they can assess correctness, fairness, and safety of the decisions.
- ✗
AI that automatically generates explanations of its errors
Why it's wrong here
Automatically explaining errors is a possible application of XAI, but the field has a much broader scope. XAI aims to make all model decisions interpretable—both correct and incorrect ones—through global explanations of overall behavior and local explanations of individual predictions, while error explanation alone would ignore the majority of outputs that are correct.
Go deeper
Related to this question
Learn chapter
Responsible AI Principles
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
Key term
Bias
Bias in AI is a systematic error in data or algorithms that leads to unfair or inaccurate outcomes, often reflecting real-world prejudices.
About these practice questions
Courseiva writes every AI-900 question from scratch — 985 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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.