Hot Exam AI-901 Success Pass Certify | High Pass-Rate AI-901 Free Updates: Microsoft Azure AI Fundamentals

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Microsoft AI-901 Exam Syllabus Topics:

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

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Microsoft Azure AI Fundamentals Sample Questions (Q71-Q76):

NEW QUESTION # 71
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 72
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

The correct answer is classify email messages as work-related or personal. Natural language processing (NLP) is concerned with analyzing, interpreting, and processing human language, including unstructured text contained in documents, messages, reviews, and emails. Text classification is therefore a direct NLP workload: an AI model analyzes the linguistic content of an email and assigns it to a predefined category such as work-related or personal.
Microsoft ' s current AI-901 study guide explicitly requires candidates to identify scenarios for common AI workloads, including text analysis, and to understand common text-analysis techniques. Microsoft Azure Language in Foundry Tools similarly supports capabilities for extracting information from text and classifying text, demonstrating that categorization based on textual content is an NLP function.
Predicting future car rentals is primarily a regression/forecasting problem because the output is a numerical quantity. Predicting whether a website visitor will transact is typically a binary classification machine- learning problem based on behavioral or transactional features, not necessarily language. Stopping industrial machinery when a temperature threshold is exceeded is a rule-based monitoring/control scenario.


NEW QUESTION # 73
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 # 74
Match the description to the corresponding Microsoft Foundry tools.
To answer, drag the appropriate description from the column on the left to its tool on the right. Each description may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Speech recognition is used to recognize spoken language and convert it into written text. Therefore, the correct match is:
Speech recognition = Transcribes spoken input
Speech synthesis, also known as text to speech, is used to convert text into spoken audio. Therefore, the correct match is:
Speech synthesis = Generates spoken output
The other options are incorrect for these two tools:
Extracts entities from the output is a text analytics / language processing task.
Translates spoken input is speech translation.
Translates written output is text translation.


NEW QUESTION # 75
You are developing an application named App1 that analyzes customer feedback.
App1 will call Azure Language in Foundry Tools from a generative AI agent in Microsoft Foundry.
You need to ensure that the agent can access Azure Language text analysis.
What should you configure?

Answer: D

Explanation:
The Azure Language MCP (Model Context Protocol) server needs to be configured so your generative AI agent can utilize text analysis tools (like sentiment analysis, keyphrase extraction, and language detection).Azure Language in Foundry Tools exposes its capabilities as standardized tools using the MCP framework. This means your agent cannot call the traditional REST API directly; it must route requests through an MCP server configuration.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/overview


NEW QUESTION # 76
......

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