Coverage Matrix

This matrix maps every official AI-901 exam skill to the challenge(s) that cover it.

How to use this matrix

Use this page as a study checklist. If you find a weak skill area, jump to the mapped challenges for targeted review, then finish with Challenge 24 as a capstone.

Domain 1: Identify AI Concepts and Capabilities (40-45%)

Challenges: 01-09

Skill	Challenge(s)
Identify features of common AI workloads (computer vision, NLP, speech, document intelligence, generative AI, anomaly detection)	Ch 01, Ch 03
Identify guiding principles for responsible AI (fairness, reliability, privacy, inclusiveness, transparency, accountability)	Ch 02
Identify features and labels in a dataset, describe ML lifecycle	Ch 05
Describe training and validation datasets	Ch 05
Identify types of ML (regression, classification, clustering)	Ch 05, Ch 06, Ch 07
Describe deep learning concepts and neural networks	Ch 08
Describe capabilities of Azure Machine Learning	Ch 09
Identify when to use ML vs pre-built AI services	Ch 04, Ch 09

Domain 2: Implement AI Solutions Using Azure Foundry (55-60%)

Challenges: 10-23

Skill	Challenge(s)
Identify features of computer vision workloads on Azure	Ch 10, Ch 11, Ch 12, Ch 13
Describe image classification, object detection, OCR	Ch 10, Ch 11, Ch 12
Describe face detection and analysis	Ch 13
Identify features of NLP workloads	Ch 14, Ch 15, Ch 16, Ch 17, Ch 18
Describe text analytics (key phrases, entities, sentiment)	Ch 14, Ch 15
Describe speech services (STT, TTS, translation)	Ch 16, Ch 17
Identify Azure AI Language and Speech service capabilities	Ch 18
Describe features of generative AI models	Ch 19
Identify common scenarios for generative AI	Ch 19, Ch 20
Describe capabilities of Azure OpenAI Service	Ch 20
Describe Azure AI Foundry capabilities	Ch 21
Describe prompt engineering concepts	Ch 22
Identify responsible AI practices for generative AI	Ch 23

Capstone

Skill	Challenge(s)
End-to-end AI-901 review and applied practice	Ch 24