Pass AI-901 Guide | AI-901 Training Questions

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Microsoft AI-901 Exam Syllabus Topics:

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

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Microsoft Azure AI Fundamentals Sample Questions (Q37-Q42):

NEW QUESTION # 37
Capturing text from images is an example of which type of AI capability?

Answer: A

Explanation:
Capturing text from images is the exact definition of optical character recognition (OCR).
How OCR Works
Image input: Software takes a photo, scan, or graphic containing letters.
Shape analysis: It scans the pixels to find light and dark areas that match letters or numbers.
Text output: It changes the visual shapes into real, editable digital text.
Reference:
https://en.wikipedia.org/wiki/Optical_character_recognition


NEW QUESTION # 38
You plan to develop an AI application that will read the license plates of motor vehicles by using Azure AI Foundry. What should you use to develop the application?

Answer: A

Explanation:
Azure AI Studio (now Azure AI Foundry) is used to develop an AI application for reading license plates using its tools and models. The platform allows developers to build, customize, and deploy AI agents and applications, including those that process images to extract information like license plate numbers.
Key components of the process
Platform: Azure AI Foundry (formerly Azure AI Studio) is the central platform for building and managing AI applications.
Tools: You can use its integrated development environment (IDE), SDKs, and APIs to develop and customize models.
Models: The platform provides access to a wide range of AI models, including computer vision models capable of optical character recognition (OCR), which can be used to read text from images.
Workflow:
Create an Azure AI Foundry project in Azure.
Use the model catalog to find and deploy a suitable computer vision or OCR model.
Integrate the model into your application using the SDK or API.
For a license plate application, you would feed images to the model, and the model would output the recognized text from the license plate.
Reference:
https://azure.microsoft.com/en-us/products/ai-foundry


NEW QUESTION # 39
You need to compare the costs of large language models (LLMs) for a generative AI solution.
What should you use in the Microsoft Foundry portal?

Answer: D

Explanation:
To compare the costs of large language models in Microsoft Foundry portal, use the Model leaderboard.
Microsoft documentation states that the model leaderboard helps compare models across quality, safety, estimated cost, and throughput. It also supports trade-off charts and side-by-side model comparison for features, performance, and estimated cost.


NEW QUESTION # 40
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:
Box: Azure Speech in Foundry Tools
Azure Speech in Foundry Tools converts spoken audio into text. This specific capability is called speech to text or Automatic Speech Recognition (ASR). It allows you to ingest audio streams or pre-recorded audio files and transcribe them into accurate, searchable text.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/speech-service/speech-to-text


NEW QUESTION # 41
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
Answer
Generative AI models predict the next token in a sequence based on patterns learned during training.
Yes
A system prompt can be used to influence the behavior and tone of the responses generated by a generative AI model.
Yes
Increasing the Temperature parameter makes the responses generated by generative AI models more deterministic and consistent.
No
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Fundamentals/Course Guide
/topics:
The first statement is Yes. Generative language models generate text autoregressively by estimating probabilities for the next token based on preceding context and patterns learned during model training. This next-token prediction mechanism is fundamental to how transformer-based generative models produce sequences of text.
The second statement is also Yes. A system prompt provides high-level instructions that establish how the model should behave. It can define role, response style, tone, constraints, and other behavioral expectations.
The AI-901 study guide explicitly includes creating effective system and user prompts as part of implementing generative AI applications in Microsoft Foundry.
The third statement is No. Increasing Temperature does the opposite of making output more deterministic.
Microsoft documents that higher temperature values increase randomness and output diversity, while lower values make responses more focused and predictable. At a temperature approaching zero, model selection becomes substantially more deterministic because higher-probability tokens are favored more strongly.


NEW QUESTION # 42
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