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Microsoft AI-901 Exam Syllabus Topics:

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

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

NEW QUESTION # 99
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

Counting the number of animals in an area based on a video feed is an example of computer vision .
Computer vision is the AI workload used to analyze visual content such as images and video. Counting animals in a video feed requires detecting visual objects in video frames and analyzing them, which is a computer vision task.
Forecasting predicts future values or trends from historical data.
Knowledge mining extracts insights from large document or data collections.
Therefore, the correct answer is computer vision .

NEW QUESTION # 100
What is an example of the Microsoft responsible Al principle of transparency?

A. helping users understand the decisions made by an Al system
B. ensuring that the privileged data of users is stored in a secure manner
C. ensuring that developers are accountable for the solutions they create
D. ensuring that opportunities are allocated equally to all applicants

Answer: A

Explanation:
The Microsoft responsible AI principle of transparency means AI systems should be understandable. Users should know when they are interacting with AI and should be able to understand the system's purpose, capabilities, limitations, and the basis for its decisions.
Therefore, helping users understand the decisions made by an AI system is an example of transparency .

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:

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 # 102
Select the answer that correctly completes the sentence.

Answer:

Explanation:

NEW QUESTION # 103
Which two scenarios are examples of a natural language processing workload? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. a smart device in the home that responds to questions such as, "What will the weather be like today?"
B. monitoring the temperature of machinery to turn on a fan when the temperature reaches a specific threshold
C. an agent on a website that uses a knowledge base to interactively respond to users' questions
D. assembly line machinery that autonomously inserts headlamps into cars

Answer: A,C

Explanation:
The Smart Home Device: It uses speech-to-text to understand voice commands, parses grammatical structures to find user intent, and generates a spoken answer using text-to-speech.
The Website Chatbot: It processes incoming typed text, matches user phrases to topics in a structured knowledge base, and serves human-like conversational text answers back to the browser.

NEW QUESTION # 104
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