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Microsoft AI-901 Exam Syllabus Topics:

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

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Microsoft Azure AI Fundamentals Sample Questions (Q34-Q39):

NEW QUESTION # 34
You are developing an application that analyzes voicemail recordings by using Azure Content Understanding in Foundry Tools.
You need to extract a transcript and structured information from the recordings.
Which type of analyzer should you use?

A. image analyzer
B. audio analyzer
C. video analyzer
D. document analyzer

Answer: B

NEW QUESTION # 35
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

The completed sentence is:
When developing an AI system for self-driving cars, the Microsoft reliability and safety principle for responsible AI should be applied to ensure consistent operation of the system during use.
Self-driving cars are safety-critical AI systems. The key requirement in the question is consistent operation of the system during use , which maps directly to the Microsoft responsible AI principle of reliability and safety .
The other options are incorrect:
Fairness focuses on reducing bias and treating people equitably.
Accountability focuses on ensuring people remain responsible for AI systems and their outcomes.
Inclusiveness focuses on designing AI systems that work for and empower diverse users, including people with different needs or abilities.
Therefore, the correct answer is reliability and safety .

NEW QUESTION # 36
What are two common use cases for generative AI solutions? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. generating draft responses for customer service agents
B. classifying email messages as spam or non-spam
C. predicting sales revenue based on historical data
D. creating original artwork from textual descriptions

Answer: A,D

Explanation:
B: Generative AI tools can assist customer service agents by automatically drafting responses to customer inquiries, providing personalized and context-aware solutions. This helps agents improve efficiency, reduce workload, and enhance customer satisfaction.
D: DALL E3, developed by OpenAI, is a cutting-edge generative AI model designed to create images from text descriptions.
Reference:
https://medium.com/@eastgate/top-7-generative-ai-models-tools-for-text-image-and-video-creation-237a0de231b1

NEW QUESTION # 37
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: A summarization model is used to transcribe spoken audio into written text. = No Summarization creates a shorter version of existing text. Transcribing spoken audio into written text is a speech recognition / speech-to-text capability, not summarization.
Statement 2: A chat completion model can generate vector embeddings for semantic tasks. = No Chat completion models are used to generate conversational text responses. Vector embeddings for semantic search, similarity, and retrieval tasks are generated by embedding models , not chat completion models.
Statement 3: An embedding model can generate vector representations of the provided input. = Yes This is correct. Embedding models convert input such as text into vector representations that capture semantic meaning and can be used for tasks such as similarity search, clustering, and retrieval.

NEW QUESTION # 38
Which functionality in Azure AI Foundry enables you to test prompts for generative AI models?

A. playgrounds
B. tracing
C. prompt flow
D. evaluation

Answer: A

Explanation:
Azure AI Foundry playgrounds are designed for testing and prototyping prompts for generative AI models. You can use these interactive, web-based interfaces to experiment with various system and user prompts, compare different model responses in real-time, and even evaluate their performance using structured metrics like similarity and groundedness.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/concept-playgrounds

NEW QUESTION # 39
......

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