Week 4— Describe Artificial Intelligence workloads and considerations · Describe fundamental principles of machine learning on Azure · Describe features of computer vision workloads on Azure · Describe features of Natural Language Processing workloads on Azure · Describe features of generative AI workloads on Azure
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Describe Artificial Intelligence workloads and considerations
This AI-900 domain covers foundational AI concepts: workloads such as computer vision, natural language processing, document intelligence, and generative AI, plus responsible AI principles like fairness, reliability, privacy, inclusiveness, transparency, and accountability. The exam tests these through scenario-based multiple-choice questions asking you to identify the correct workload type or responsible AI principle for a given business situation.
📅 Days 25–28🎯 ~34 questions/day⚖ 19% of exam- ✓Matching scenarios to Azure AI workloads: vision, language, document intelligence, generative AI
- ✓Identifying responsible AI principles: fairness, reliability and safety, privacy, inclusiveness, transparency, accountability
- ✓Recognizing Azure services like Azure AI Vision, Azure AI Language, and Azure OpenAI
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Describe fundamental principles of machine learning on Azure
This domain covers core machine learning concepts on Azure: regression, classification, clustering, and the Azure Machine Learning workflow. The exam tests whether you can choose the right model type for a scenario, identify Azure Machine Learning components like compute, datasets, and pipelines, and distinguish automated ML from designer. Expect scenario-based questions rather than deep coding.
📅 Days 25–28🎯 ~35 questions/day⚖ 19% of exam- ✓Selecting regression, classification, or clustering for a given business scenario
- ✓Identifying Azure Machine Learning workspace assets: datasets, compute targets, and environments
- ✓Using Automated Machine Learning to train and deploy models with minimal code
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Describe features of computer vision workloads on Azure
This domain covers Azure's prebuilt and custom computer vision capabilities: image classification, object detection, OCR, facial detection, and spatial analysis. AI-900 tests it through scenario questions asking which Azure AI Vision or Face service feature fits a described workload, and whether a prebuilt or custom model is appropriate. Expect questions on service selection and capability matching rather than implementation detail.
📅 Days 25–28🎯 ~35 questions/day⚖ 19% of exam- ✓Choosing between Azure AI Vision, Face, and Document Intelligence for a given scenario
- ✓Distinguishing image classification, object detection, and semantic segmentation capabilities
- ✓Recognizing OCR, Read API, and handwriting extraction use cases in Azure AI Vision
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Describe features of Natural Language Processing workloads on Azure
Use this page to practise NAT and PAT questions. Understanding the difference between inside local and inside global addresses — and when each NAT type is appropriate — is the fastest way to eliminate wrong answers.
📅 Days 25–28🎯 ~34 questions/day⚖ 19% of exam- ✓Static NAT, dynamic NAT and PAT behaviour.
- ✓Inside local, inside global, outside local and outside global address meanings.
- ✓How NAT affects connectivity between private and public destinations.
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Describe features of generative AI workloads on Azure
This AI-900 domain covers generative AI concepts and how Azure delivers them, chiefly Azure OpenAI Service and Azure AI Foundry. The exam tests your ability to distinguish generative models from discriminative ones, identify responsible AI concerns like hallucination, and match Azure generative features such as prompts, completions, and deployments to described scenarios.
📅 Days 25–28🎯 ~35 questions/day⚖ 24% of exam- ✓Distinguishing generative AI, which creates new content, from discriminative models that classify or predict labels
- ✓Identifying Azure OpenAI Service capabilities including GPT models, DALL-E image generation, and embeddings
- ✓Recognizing prompt engineering basics: system, user, and assistant messages plus completion outputs