Valid AI-901 Test Practice - AI-901 Valid Exam Prep

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Microsoft AI-901 Exam Syllabus Topics:

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

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Microsoft Azure AI Fundamentals Sample Questions (Q61-Q66):

NEW QUESTION # 61
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 # 62
Hotspot Question
You are developing an application that analyze invoices by using Azure Content Understanding in Foundry Tools.
You need to ensure that the application retrieves the analysis results after processing completes.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 63
You are developing a web app that processes invoices to calculate expenses.
You need to extract structured fields, including nested values, from the invoices by using a defined schema.
What should you use?

Answer: B

Explanation:
An analyzer in Azure Content Understanding within Foundry Tools can be used for this scenario.
Prebuilt Invoice Support: It offers prebuilt invoice analyzer templates specifically trained to identify common transactional data out of the box.
Schema-Driven Extraction: Instead of manual prompt engineering or tedious labeling, you can supply a strict JSON schema containing the specific fields you need.
Nested Values & Layouts: It handles hierarchical, nested elements (such as itemized tables, nested line items, or customer details) and maps them cleanly into arrays or structured objects.
Web App Integration: Activating the analyzer creates a highly scalable REST API endpoint. Your invoice-processing web app can query this endpoint to receive clean, machine-readable structured JSON payloads ready for downstream expense calculation.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/overview


NEW QUESTION # 64
You need to deploy a generative AI model to a Microsoft Foundry project. Which factor should you use to identify which type of model can be deployed to the project?

Answer: D

Explanation:
Yes, Azure region availability determines whether you can use a Global, Data Zone, or Regional/Standard deployment type for your generative AI model.
Deployment Types and Region Dependencies
Global Deployments (Global Standard / Global Provisioned): Use these when there are no strict single-region data residency constraints; traffic and inferencing may route to any Azure region where the model is actively supported and has capacity.
Data Zone Deployments (Data Zone Standard / Data Zone Provisioned): Use these when data processing must be restricted to a specific compliance boundary (such as the EU or US data zone), requiring your project to reside in a region belonging to that designated zone.
Regional Deployments (Standard / Regional Provisioned): Use these when your workload requires data processing to stay strictly within the single Azure region associated with your project.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/reference/region-support


NEW QUESTION # 65
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 # 66
......

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