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Microsoft AI-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Describe Artificial Intelligence workloads and considerations	20-25%	- Responsible AI principles 1. Reliability and safety 2. Privacy and security 3. Transparency and accountability 4. Inclusiveness 5. Fairness - Identify features of common AI workloads 1. Computer vision use cases 2. Document processing and extraction 3. Conversational AI and chatbots 4. Natural language processing use cases
Topic 2: Describe features of generative AI workloads on Azure	10-15%	- Azure OpenAI and generative services 1. Use cases: chatbots, content generation, summarization 2. Azure OpenAI Service capabilities - Generative AI concepts 1. Large language models (LLMs) 2. Responsible use of generative AI 3. Prompt engineering basics
Topic 3: Describe features of computer vision workloads on Azure	15-20%	- Azure AI Vision services 1. Face detection and analysis (limited use cases) 2. Azure AI Vision - Image analysis 1. Object detection 2. Image classification 3. Optical character recognition (OCR)
Topic 4: Describe fundamental principles of machine learning on Azure	25-30%	- Azure Machine Learning basics 1. Azure ML workspace components 2. AutoML concepts - Machine learning concepts 1. Regression and classification 2. Training and evaluation concepts 3. Supervised vs unsupervised learning
Topic 5: Describe features of natural language processing (NLP) workloads on Azure	15-20%	- Text analysis 1. Sentiment analysis 2. Language detection 3. Key phrase extraction - Azure NLP services 1. Azure AI Language 2. Speech services (speech-to-text, text-to-speech)

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Microsoft Azure AI Fundamentals Sample Questions (Q99-Q104):

NEW QUESTION # 99
You are developing a lightweight application that will call an agent programmatically by using the Microsoft Foundry SDK. You need to identify the agent ID. What should you do?

A. In the Foundry tool catalog, copy the tool name used by the agent.
B. In the Foundry playground, select Code, and view the .env variables.
C. In the model catalog, open the model card and copy the model family name.
D. the Foundry portal, copy the project name.

Answer: B

Explanation:
When calling a Foundry agent programmatically by using the Microsoft Foundry SDK, the application needs the agent's configuration values, including the agent ID . The Foundry playground provides a Code view that shows sample application code and environment variables needed to call the agent.
The other options are incorrect:
A). copy the tool name used by the agent identifies a tool, not the agent.
B). copy the model family name identifies the model family, not the agent instance.
C). copy the project name identifies the project, not the specific agent ID.

NEW QUESTION # 100
You are developing an application that continuously transcribes speech from a default microphone by using Azure Speech in Microsoft Foundry Tools.
You need to perform speech-to-text transcription from the microphone.
Which Python class should you use?

A. SpeechSynthesizer
B. SpeechRecognizer
C. SpeechConfig
D. AudioOutputConfig

Answer: B

NEW QUESTION # 101
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:

NEW QUESTION # 102
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:

NEW QUESTION # 103
Hotspot Question
You are developing an application that analyzes invoices by using Azure Content Understanding in Foundry Tools.
You have a custom analyzer named invoiceAnalyzer.
You need to use the analyzer to process invoice files.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box: analyzer_id
Core Method Signature
poller = client.begin_analyze(
analyzer_id="YOUR_ANALYZER_ID",
inputs=[AnalysisInput(url="FILE_URL")],
**kwargs
)
Note:
client.begin_analyze is the primary method used in the official Azure AI Content Understanding Python SDK (azure-ai-contentunderstanding) to kick off asynchronous, long-running operations (LRO) for extracting structured data and layout information from multimodal files.
Formerly a part of standalone Azure AI services, Content Understanding is fully integrated into the Microsoft Foundry ecosystem. This service utilizes foundational large language models deployed inside your Foundry resource to analyze documents, images, audio, and video streams based on prebuilt or custom schemas.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/quickstart/use-rest-api

NEW QUESTION # 104
......

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