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Microsoft AI-901 Exam Syllabus Topics:

SectionWeightObjectives
Implement AI solutions by using Microsoft Foundry55-60%- Get started with Microsoft Foundry
  • 1. Describe core features and use cases of Microsoft Foundry
  • 2. Create and configure a Foundry project
  • 3. Navigate the Microsoft Foundry portal
- Implement AI solutions
  • 1. Monitor and manage AI solutions
  • 2. Use code examples to call AI services
  • 3. Integrate AI services into applications
  • 4. Implement single-agent solutions
- Work with models in Microsoft Foundry
  • 1. Customize models with prompt engineering
  • 2. Fine-tune models
  • 3. Evaluate model performance and safety
  • 4. Discover and deploy foundation models
Identify AI concepts and responsibilities40-45%- Identify AI concepts and features
  • 1. Describe capabilities of natural language processing
  • 2. Describe capabilities of machine learning
  • 3. Describe capabilities of computer vision
  • 4. Describe types of AI workloads
  • 5. Describe characteristics of generative AI
- Describe principles of responsible AI
  • 1. Describe reliability and safety considerations
  • 2. Describe fairness considerations
  • 3. Describe privacy and security considerations
  • 4. Describe inclusiveness considerations
  • 5. Describe transparency considerations
  • 6. Describe accountability considerations
- Identify AI model components and configurations
  • 1. Describe model deployment and consumption concepts
  • 2. Describe data preparation for AI models
  • 3. Describe model training and evaluation concepts

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Microsoft Azure AI Fundamentals Sample Questions (Q98-Q103):

NEW QUESTION # 98
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 99
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Statement 1: Evaluators in Microsoft Foundry replace the need for configuring token limits. = No Evaluators are used to assess model or agent output quality, safety, and reliability. They do not replace model configuration settings such as token limits. Microsoft describes evaluators as tools that measure the quality, safety, and reliability of AI responses.
Statement 2: Evaluators in Microsoft Foundry can assess the quality and safety of responses generated by a generative AI model. = Yes This is correct. Microsoft Foundry includes built-in evaluators for general quality metrics such as coherence and fluency, safety/security metrics, RAG metrics, and agent-specific metrics.
Statement 3: Evaluators in Microsoft Foundry can retrain a deployed generative AI model automatically when quality issues are detected. = No Evaluators measure and report quality, safety, and reliability issues. They do not automatically retrain deployed generative AI models. Microsoft describes evaluation as measuring model or agent performance against test data, while monitoring can alert when outputs fail quality thresholds.


NEW QUESTION # 100
Capturing text from images is an example of which type of AI capability?

Answer: A

Explanation:
Optical Character Recognition (OCR) is a technology that converts images of text (like scanned documents or photos) into machine-readable, editable text. It's used to extract text from various sources and make it accessible for digital editing, searching, and other manipulations.
Reference:
https://www.ibm.com/think/topics/optical-character-recognition


NEW QUESTION # 101
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 102
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:
The correct answer is API endpoint . After a generative AI model is deployed in Microsoft Foundry, the deployment exposes an inference endpoint through which external applications can submit requests and receive generated responses. Microsoft Foundry documentation describes deployed models as being accessible programmatically through an endpoint and associated credentials. Applications invoke the relevant REST API or SDK against this endpoint and specify the deployed model as required by the API.
An embedding vector is a numerical representation of semantic information used for similarity search, retrieval, and grounding; it is not the network interface through which a deployed model is invoked. A training dataset contains data used to train or fine-tune a model and is not called when performing inference.
A URL parameter can be part of an API request, but it does not represent the deployed model ' s callable interface.
This directly aligns with the AI-901 objective to deploy a model and interact with it in the Foundry portal and to create lightweight applications that invoke deployed generative AI models through supported APIs and SDKs.


NEW QUESTION # 103
......

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