AI-901 Azure AI Fundamentals: Applied Practice Tests

Categories: Microsoft Azure
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About Course

This course contains the use of artificial intelligence.

Choosing an AI capability is only part of building an application. You also need to handle the returned evidence, recognise missing results and apply the right safeguards. These AI-901 Azure AI Fundamentals practice tests connect AI concepts with lightweight implementation decisions in Microsoft Foundry and Azure AI services.

Practise what changes the answer

Questions ask you to compare model and deployment choices, inspect group-level errors hidden by an aggregate result, and distinguish an agent's permission from the current state needed to complete an action. Python examples address chat clients, prompts, agent state, text and audio results, Speech lifecycles, Vision calls and Content Understanding extraction.

Feedback for every option explains which assumption fits or fails. Overall explanations and review suggestions help you identify the stage that needs attention: input handling, service selection, result interpretation or an action prerequisite. Familiarity with basic Python will help you read these scenarios.

Use concepts and implementation together

The 300 questions are organised into four focused 50-question tests and two mixed 50-question mocks with 45-minute timers. Begin with the focused area that exposes your weakest reasoning, review missed and guessed answers, then use a mixed mock to practise moving between foundations and implementation.

The fixed mock length is a course design choice; the real exam's question count varies. The 80% practice target is not a conversion of Microsoft's scaled passing score. These selection questions do not reproduce every exam interaction or partial-credit rule. This independent resource includes no exam voucher or paid cloud lab, and an Azure subscription is not required to take the tests. It is not endorsed by Microsoft.

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What Will You Learn?

  • Apply responsible AI principles to evidence, access, fallback, accessibility and accountability decisions.
  • Compare supported model and deployment choices using modality, processing boundary, capacity and latency needs.
  • Interpret Foundry prompts, project endpoints, response text, agent versions and conversation state.
  • Handle Text Analytics errors, identifiers, source spans, audio encodings and Speech SDK events.
  • Process image inputs and generated outputs while preserving artifact and source identity.
  • Interpret Content Understanding fields, model mappings, source estimates and application calculations.
  • Use timed mixed practice to identify which conditions and result-processing steps need further study.

Course Content

Practice tests

  • AI Foundations, Responsible Use and Deployment
  • Foundry Prompts, Chat Clients and Agents
  • Text Analytics, Audio and Speech Applications
  • Vision and Content Understanding Applications
  • Mixed Mock 1 — AI-901
  • Mixed Mock 2 — AI-901