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Microsoft AI-901 Exam Syllabus Topics:

SectionWeightObjectives
Describe Artificial Intelligence workloads and considerations (15-20%)15-20%- Identify features of common AI workloads
  • 1. Identify features of knowledge mining workloads
  • 2. Identify features of content moderation and moderation workloads
  • 3. Identify features of document intelligence workloads
  • 4. Identify features of Natural Language Processing (NLP) workloads
  • 5. Identify features of computer vision workloads
  • 6. Identify features of generative AI workloads
- Identify guiding principles for responsible AI
  • 1. Describe considerations for accountability in an AI solution
  • 2. Describe considerations for fairness in an AI solution
  • 3. Describe considerations for reliability and safety in an AI solution
  • 4. Describe considerations for transparency in an AI solution
  • 5. Describe considerations for inclusiveness in an AI solution
  • 6. Describe considerations for privacy and security in an AI solution
Describe features of generative AI workloads on Azure (20-25%)20-25%- Identify generative AI services and capabilities in Microsoft Azure
  • 1. Describe features and capabilities of Azure AI Foundry model catalog
  • 2. Describe features and capabilities of Azure OpenAI service
  • 3. Describe features and capabilities of Azure AI Foundry
- Identify features of generative AI solutions
  • 1. Identify responsible AI considerations for generative AI
  • 2. Identify common scenarios for generative AI
  • 3. Identify features of generative AI models
Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)15-20%- Identify Azure tools and services for NLP workloads
  • 1. Describe capabilities of the Azure AI Language service
  • 2. Describe capabilities of the Azure AI Speech service
- Identify features of common NLP workload scenarios
  • 1. Identify features and uses for language modeling
  • 2. Identify features and uses for sentiment analysis
  • 3. Identify features and uses for entity recognition
  • 4. Identify features and uses for speech recognition and synthesis
  • 5. Identify features and uses for translation
  • 6. Identify features and uses for key phrase extraction
Describe fundamental principles of machine learning on Azure (15-20%)15-20%- Identify common machine learning techniques
  • 1. Identify features of deep learning techniques
  • 2. Identify regression machine learning scenarios
  • 3. Identify classification machine learning scenarios
  • 4. Identify clustering machine learning scenarios
- Describe core machine learning concepts
  • 1. Identify features and labels for training data in machine learning
  • 2. Describe how training and validation datasets are used in machine learning
- Describe Azure Machine Learning capabilities
  • 1. Describe model management and deployment capabilities in Azure Machine Learning
  • 2. Describe capabilities of automated machine learning
  • 3. Describe data and compute services for data science and machine learning
Describe features of computer vision workloads on Azure (15-20%)15-20%- Identify Azure tools and services for computer vision tasks
  • 1. Describe capabilities of the Azure AI Face detection service
  • 2. Describe capabilities of the Azure AI Vision service
- Identify common types of computer vision solution
  • 1. Identify features of image classification solutions
  • 2. Identify features of object detection solutions
  • 3. Identify features of semantic segmentation solutions
  • 4. Identify features of face detection and identification solutions
  • 5. Identify features of optical character recognition (OCR) solutions
  • 6. Identify features of image analysis solutions

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Microsoft Azure AI Fundamentals Sample Questions (Q94-Q99):

NEW QUESTION # 94
You have a Microsoft Foundry project named project1 that contains an Azure OpenAI resource named Resource1.
To Resource1, you deploy a gpt-4.1-mini model by using a model deployment named my-mini-gpt.
You need to connect to my-mini-gpt from an application.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets.
Each value may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
client = OpenAI(
api_key= " ... " ,
base_url= " https://resource1.openai.azure.com/openai/v1/ " ,
)
response = client.responses.create(
model= " my-mini-gpt " ,
)
For Azure OpenAI in Microsoft Foundry, the base_url uses the Azure OpenAI resource name in the endpoint format:
https:// < resource-name > .openai.azure.com/openai/v1/
In the question, the Azure OpenAI resource is named Resource1 , so the first blank must be resource1 .
Microsoft documentation for Azure OpenAI v1 endpoints confirms that the endpoint must use the ...openai.
azure.com/openai/v1/ path.
For the model parameter, Azure OpenAI requires the deployment name , not the underlying model name.
Microsoft states that Azure OpenAI always requires the deployment name when calling APIs, even when the parameter is named model.
The deployed model is gpt-4.1-mini , but the deployment name is my-mini-gpt . Therefore, the second blank must be:
model= " my-mini-gpt "
So the correct selections are:
base_url blank = resource1
model blank = my-mini-gpt


NEW QUESTION # 95
You need to build an AI solution that generates marketing email drafts based on a short description of a product and its target audience. Which AI workload should you use?

Answer: B

Explanation:
Generating marketing email drafts from a short product description and target audience is a content generation task. This is a generative AI workload because the system creates new text based on the user's prompt.


NEW QUESTION # 96
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 97
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 98
Hotspot Question
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:
Box 1: No
No, an Azure Content Understanding analyzer does not return results in HTML format. Instead, it processes your input files (which can include HTML) and structures the extracted output into Markdown, JSON fields, and segments.
Box 2: No
Only if you use the newly released synchronous operations introduced in the 2026-06-01-preview API version. If you are using the standard or generally available (GA) versions of the API, the process runs asynchronously by default.
Box 3: Yes
Yes, Azure Content Understanding in Foundry Tools can ingest and extract structured information from documents, images, audio, and video files Multimodal Ingestion: Processes unstructured data across text, images, audio recordings, and video files into user-defined formats like JSON or Markdown.
Schema-Driven Extraction: Uses prebuilt or custom schemas to extract, classify, or generate specific fields without complex prompt engineering.
Confidence & Grounding: Returns confidence scores and source grounding to verify accuracy and minimize manual oversight.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/concepts/analyzer-reference
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/whats-new


NEW QUESTION # 99
......

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