Coverage Matrix
This matrix maps every official AI-900 exam skill to the challenge(s) that cover it. Use this to identify gaps in your preparation and target specific challenges for review.
Domain 1: Describe AI workloads and considerations (15–20%)
Identify features of common AI workloads
| Skill | Challenge(s) |
|---|---|
| Identify features of anomaly detection workloads | Ch 01 |
| Identify computer vision workloads | Ch 01, Ch 03 |
| Identify natural language processing workloads | Ch 01, Ch 03 |
| Identify knowledge mining workloads | Ch 01, Ch 03 |
| Identify generative AI workloads | Ch 01, Ch 03 |
| Identify document intelligence workloads | Ch 01, Ch 03 |
Identify guiding principles for responsible AI
| Skill | Challenge(s) |
|---|---|
| Describe fairness considerations | Ch 02 |
| Describe reliability and safety considerations | Ch 02 |
| Describe privacy and security considerations | Ch 02 |
| Describe inclusiveness considerations | Ch 02 |
| Describe transparency considerations | Ch 02 |
| Describe accountability considerations | Ch 02 |
Domain 2: Describe fundamental principles of machine learning on Azure (15–20%)
Describe fundamental principles of machine learning
| Skill | Challenge(s) |
|---|---|
| Identify features and labels in a dataset | Ch 05 |
| Describe how training and validation datasets are used | Ch 05 |
| Describe how machine learning algorithms are used for model training | Ch 05 |
| Describe model inference and deployment | Ch 05 |
Describe Azure Machine Learning capabilities
| Skill | Challenge(s) |
|---|---|
| Describe capabilities of Azure Machine Learning | Ch 09 |
| Describe data and compute resources in Azure ML | Ch 09 |
| Describe jobs and pipelines in Azure ML | Ch 09 |
Machine learning model types
| Skill | Challenge(s) |
|---|---|
| Describe regression models — prediction of continuous values | Ch 05 |
| Describe classification models — binary and multi-class | Ch 06 |
| Describe clustering models — unsupervised grouping | Ch 07 |
| Describe deep learning concepts | Ch 08 |
Domain 3: Describe features of computer vision workloads on Azure (15–20%)
Identify types of computer vision solutions
| Skill | Challenge(s) |
|---|---|
| Identify types of computer vision solutions | Ch 10, Ch 11 |
| Describe Azure AI Vision service features | Ch 10 |
Image analysis
| Skill | Challenge(s) |
|---|---|
| Describe image classification | Ch 10 |
| Describe object detection | Ch 11 |
| Describe optical character recognition (OCR) | Ch 12 |
| Describe facial detection and analysis | Ch 13 |
Domain 4: Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)
Describe NLP capabilities
| Skill | Challenge(s) |
|---|---|
| Describe key phrase extraction | Ch 14 |
| Describe entity recognition | Ch 14 |
| Describe sentiment analysis | Ch 15 |
| Describe language modeling | Ch 15 |
| Describe speech recognition and synthesis | Ch 16 |
| Describe translation capabilities | Ch 17 |
Azure AI Language and Speech services
| Skill | Challenge(s) |
|---|---|
| Describe capabilities of Azure AI Language | Ch 18 |
| Describe capabilities of Azure AI Speech | Ch 16, Ch 18 |
Domain 5: Describe features of generative AI workloads on Azure (20–25%)
Describe generative AI concepts
| Skill | Challenge(s) |
|---|---|
| Describe generative AI fundamentals | Ch 19 |
| Identify features of large language models (LLMs) | Ch 19 |
| Describe Azure OpenAI Service | Ch 20 |
| Describe Azure AI Foundry | Ch 21 |
| Describe prompt engineering concepts | Ch 22 |
| Describe responsible generative AI considerations | Ch 23 |
Summary: Challenge coverage
| Challenge | Primary domain | Key topics |
|---|---|---|
| Ch 01 | Domain 1 | AI workloads overview |
| Ch 02 | Domain 1 | Responsible AI principles |
| Ch 03 | Domain 1 | AI workloads deep-dive |
| Ch 04 | Domain 1 | Azure AI Services Overview |
| Ch 05 | Domain 2 | ML fundamentals, regression |
| Ch 06 | Domain 2 | Classification |
| Ch 07 | Domain 2 | Clustering |
| Ch 08 | Domain 2 | Deep learning |
| Ch 09 | Domain 2 | Azure Machine Learning |
| Ch 10 | Domain 3 | Image analysis, classification |
| Ch 11 | Domain 3 | Object detection |
| Ch 12 | Domain 3 | OCR |
| Ch 13 | Domain 3 | Face detection |
| Ch 14 | Domain 4 | Key phrase extraction, entity recognition |
| Ch 15 | Domain 4 | Sentiment analysis, language modeling |
| Ch 16 | Domain 4 | Speech recognition and synthesis |
| Ch 17 | Domain 4 | Translation |
| Ch 18 | Domain 4 | Azure AI Language & Speech services |
| Ch 19 | Domain 5 | Generative AI fundamentals |
| Ch 20 | Domain 5 | Azure OpenAI Service |
| Ch 21 | Domain 5 | Azure AI Foundry |
| Ch 22 | Domain 5 | Prompt engineering |
| Ch 23 | Domain 5 | Responsible generative AI |
| Ch 24 | All | Capstone challenge — full exam simulation |
tip
Use this matrix to identify weak areas. If a domain feels unfamiliar, work through its challenges in sequence. If you're confident in a domain, jump straight to the later challenges for that section to validate your knowledge.