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⚡ AI-900 EXAM • MICROSOFT AZURE AI FUNDAMENTALS

Azure AI Fundamentals
(AI-900) Exam Simulator

Explore Microsoft's AI ecosystem: Responsible AI principles, Azure Automated Machine Learning, Azure AI Vision, Natural Language Processing, Speech, and Generative AI with Azure OpenAI Service.

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500+ AI-900 Questions
700 / 1000 Passing Score
60 min 45-Question Mock
14 Days 14-Day free trial
Interactive Sample

Microsoft Responsible AI Guiding Principles

Assess adherence to Microsoft's ethical framework for artificial intelligence:

Domain 1: AI Workloads & Principles Sample Question 1 of 1
A healthcare system builds an AI diagnosis model to triage chest X-rays. Medical directors verify that the model achieves uniform diagnostic accuracy across patient demographics regardless of gender, race, or age. Which Microsoft Responsible AI principle is primarily upheld?
A
Transparency
B
Fairness
C
Accountability
D
Inclusiveness
✓ Official Explanation: (B) The principle of Fairness ensures that AI systems treat all individuals fairly and without demographic bias, avoiding disproportionate errors or discrimination against protected sub-groups.
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Official Exam Blueprint

Domain Breakdown & Weighting

The simulator strictly replicates official exam domain distributions:

Domain 1: AI Workloads & Considerations

20-25%

Machine learning, computer vision, NLP, conversational AI, and the 6 Microsoft Responsible AI principles (Fairness, Reliability, Privacy, Inclusiveness, Transparency, Accountability).

Domain 2: Machine Learning Fundamentals

25-30%

Regression, classification, and clustering problems, Automated ML, evaluation metrics (R2, accuracy, precision, recall), and Azure Machine Learning designer.

Domain 3: Computer Vision Workloads

15-20%

Optical Character Recognition (OCR), image classification, object detection, spatial analysis, and Azure AI Vision capabilities.

Domain 4: Natural Language Processing & Generative AI

25-30%

Key phrase extraction, sentiment analysis, entity recognition, speech synthesis, Azure OpenAI Service (GPT models, prompt engineering, and embeddings).

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