Exam AI-901 Online - Latest AI-901 Practice Questions

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Microsoft AI-901 Exam Syllabus Topics:

SectionWeightObjectives
Describe fundamental principles of machine learning on Azure (15-20%)15-20%- 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
- Identify common machine learning techniques
  • 1. Identify clustering machine learning scenarios
  • 2. Identify classification machine learning scenarios
  • 3. Identify features of deep learning techniques
  • 4. Identify regression machine learning scenarios
- 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
Describe features of generative AI workloads on Azure (20-25%)20-25%- Identify features of generative AI solutions
  • 1. Identify features of generative AI models
  • 2. Identify responsible AI considerations for generative AI
  • 3. Identify common scenarios for generative AI
- 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
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 speech recognition and synthesis
  • 2. Identify features and uses for sentiment analysis
  • 3. Identify features and uses for language modeling
  • 4. Identify features and uses for translation
  • 5. Identify features and uses for entity recognition
  • 6. Identify features and uses for key phrase extraction
- 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
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 analysis solutions
  • 2. Identify features of object detection solutions
  • 3. Identify features of face detection and identification solutions
  • 4. Identify features of image classification solutions
  • 5. Identify features of optical character recognition (OCR) solutions
  • 6. Identify features of semantic segmentation solutions
Describe Artificial Intelligence workloads and considerations (15-20%)15-20%- Identify guiding principles for responsible AI
  • 1. Describe considerations for privacy and security in an AI solution
  • 2. Describe considerations for fairness in an AI solution
  • 3. Describe considerations for transparency in an AI solution
  • 4. Describe considerations for accountability in an AI solution
  • 5. Describe considerations for reliability and safety in an AI solution
  • 6. Describe considerations for inclusiveness in an AI solution
- Identify features of common AI workloads
  • 1. Identify features of content moderation and moderation workloads
  • 2. Identify features of document intelligence workloads
  • 3. Identify features of Natural Language Processing (NLP) workloads
  • 4. Identify features of generative AI workloads
  • 5. Identify features of knowledge mining workloads
  • 6. Identify features of computer vision workloads

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Microsoft Azure AI Fundamentals Sample Questions (Q131-Q136):

NEW QUESTION # 131
You are building an AI-based loan approval app.
You need to ensure that the app documents why a loan is approved or rejected and makes the report available to the applicant.
This is an example of which Microsoft responsible AI principle?

Answer: C

Explanation:
Transparency: AI systems should be transparent and understandable. AI systems might help inform decisions that have great impact on people's lives, so it's important that people understand how these decisions are made.
Reference:
https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/strategy/responsible-ai


NEW QUESTION # 132
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 # 133
You are building a knowledge base by using Azure AI Language service's custom answering feature. Which file format can you use to populate the knowledge base?

Answer: B

Explanation:
Azure AI Language service's custom answering can populate its knowledge base using various file formats, including structured text (.txt, .tsv), Microsoft Office documents (.docx, .xlsx, .pptx), and HTML, PDF, and Markdown files.
Reference:
https://docs.azure.cn/en-us/ai-services/language-service/question-answering/reference/document-format-guidelines


NEW QUESTION # 134
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 # 135
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You use the Azure OpenAI Responses API to send a prompt to the model.
You need to provide an image for analysis.
Which content item should you include in the request?

Answer: A


NEW QUESTION # 136
......

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