practice · AI-901
AI-901 Practice Exam 2: Microsoft Azure AI Fundamentals
Prepare for Microsoft AI-901 with 45 new scenario-based Azure AI Fundamentals questions, timed answer pauses, and source-backed explanations. This second full practice exam covers responsible AI, generative AI, Microsoft Foundry, agents, language, speech, vision, and information extraction. Use it to identify weak areas, understand why each answer works, and practice applying concepts instead of memorizing answer letters.
Video summary
Prepare for Microsoft AI-901 with 45 new scenario-based Azure AI Fundamentals questions, timed answer pauses, and source-backed explanations.
Study notes
- Chapter coverage includes Accessible speech assistant and Classification, detection, OCR, and generation.
- Later coverage includes Entity and key-phrase analysis pipeline and Confidence review and next steps.
Chapters
- Welcome and exam strategy
- Q1: Accessible speech assistant
- Q2: Loan-support recommendation monitoring
- Q3: Grounded maintenance assistant
- Q4: Customer-call summarization data boundary
- Q5: AI-generated case summary disclosure
- Q6: Agent tool-action approval
- Q7: Token-by-token generation and context
- Q8: Context limits and unsupported claims
- Q9: Small text model versus multimodal model
- Q10: Temperature and response variability
- Q11: Deployment name, endpoint, and authentication
- Q12: Agentic versus single-turn generative AI
- Q13: Key phrases versus named entities
- Q14: Summarization versus information extraction
- Q15: Live captions and spoken playback
- Q16: Classification, detection, OCR, and generation
- Q17: Text/image/audio/video extraction
- Q18: Generation versus extraction
- Q19: Route multilingual media intake
- Q20: Policy-constrained assistant prompts
- Q21: Grounded answer contract
- Q22: Portal deployment diagnosis
- Q23: Chat client configuration
- Q24: Request/response flow
- Q25: Create and test one tool-using agent
- Q26: Agent tool safety boundary
- Q27: Agent client versus model client
- Q28: Agent conversation and tool result flow
- Q29: Evaluate grounded assistant
- Q30: Authentication failure diagnosis
- Q31: Entity and key-phrase analysis pipeline
- Q32: Batch feedback analysis
- Q33: Spoken question to deployed multimodal response
- Q34: Speech recognition app flow
- Q35: Speech synthesis app flow
- Q36: Image-plus-text inspection client
- Q37: Visual prompt reliability
- Q38: Image-generation request
- Q39: Vision client request/response
- Q40: OCR versus multimodal interpretation route
- Q41: Schema-based onboarding extraction
- Q42: Extract fields from photographed labels
- Q43: Recorded inspection extraction
- Q44: Audio extraction versus transcription
- Q45: End-to-end extraction client