SAP

Azure AI Fundamentals (AI-900)

SAP

Last Updated

22 December 2024

Rating

4.7 (2160)

Exam Code: AI-900
Exam Name: Azure AI Fundamentals

Passing Score: 70%
Duration: 45 Minute

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Total Questions in Exams: 65
Total Questions in Dumps: 138

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Course Description

Azure AI Engineer Associate

As a Microsoft Azure AI engineer, you build, manage, and deploy AI solutions that leverage Azure AI.

Your responsibilities include participating in all phases of AI solutions development, including:
Requirements definition and design
Development
Deployment
Integration
Maintenance
Performance tuning
Monitoring

You work with solution architects to translate their vision. You also work with data scientists, data engineers, Internet of Things (IoT) specialists, infrastructure administrators, and other software developers too:

Build complete and secure end-to-end AI solutions.
Integrate AI capabilities in other applications and solutions.

As an Azure AI engineer, you have experience developing solutions that use languages such as:

Python
C#

You should be able to use Representational State Transfer (REST) APIs and SDKs to build secure image processing, video processing, natural language processing, knowledge mining, and generative AI solutions on Azure. You should:

Understand the components that make up the Azure AI portfolio and the available data storage options.
Be able to apply responsible AI principles.

Skills measured

Plan and manage an Azure AI solution
Implement decision support solutions
Implement computer vision solutions
Implement natural language processing solutions
Implement knowledge mining and document intelligence solutions
Implement generative AI solutions

Plan and manage an Azure AI solution (15–20%)

Select the appropriate Azure AI service

Select the appropriate service for a computer vision solution
Select the appropriate service for a natural language processing solution
Select the appropriate service for a speech solution
Select the appropriate service for a generative AI solution
Select the appropriate service for a document intelligence solution
Select the appropriate service for a knowledge mining solution

Plan, create and deploy an Azure AI service

Plan for a solution that meets Responsible AI principles
Create an Azure AI resource
Determine a default endpoint for a service
Integrate Azure AI services into a continuous integration and continuous delivery (CI/CD) pipeline
Plan and implement a container deployment

Manage, monitor, and secure an Azure AI service

Configure diagnostic logging
Monitor an Azure AI resource
Manage costs for Azure AI services
Manage account keys
Protect account keys by using Azure Key Vault
Manage authentication for an Azure AI Service resource
Manage private communications

Implement content moderation solutions (10–15%)

Create solutions for content delivery

Implement a text moderation solution with Azure AI Content Safety
Implement an image moderation solution with Azure AI Content Safety

Implement computer vision solutions (15–20%)

Analyze images

Select visual features to meet image processing requirements
Detect objects in images and generate image tags
Include image analysis features in an image processing request
Interpret image processing responses
Extract text from images using Azure AI Vision
Convert handwritten text using Azure AI Vision

Implement custom computer vision models by using Azure AI Vision

Choose between image classification and object detection models
Label images
Train a custom image model, including image classification and object detection
Evaluate custom vision model metrics
Publish a custom vision model
Consume a custom vision model

Analyze videos

Use Azure AI Video Indexer to extract insights from a video or live stream
Use Azure AI Vision Spatial Analysis to detect presence and movement of people in video

Implement natural language processing solutions (30–35%)

Analyze text by using Azure AI Language

Extract key phrases
Extract entities
Determine sentiment of text
Detect the language used in text
Detect personally identifiable information (PII) in text

Process speech by using Azure AI Speech

Implement text-to-speech
Implement speech-to-text
Improve text-to-speech by using Speech Synthesis Markup Language (SSML)
Implement custom speech solutions
Implement intent recognition
Implement keyword recognition

Translate language

Translate text and documents by using the Azure AI Translator service
Implement custom translation, including training, improving, and publishing a custom model
Translate speech-to-speech by using the Azure AI Speech service
Translate speech-to-text by using the Azure AI Speech service
Translate to multiple languages simultaneously

Implement and manage a language understanding model by using Azure AI Language

Create intents and add utterances
Create entities
Train, evaluate, deploy, and test a language understanding model
Optimize a language understanding model
Consume a language model from a client application
Backup and recover language understanding models

Create a question answering solution by using Azure AI Language

Create a question answering project
Add question-and-answer pairs manually
Import sources
Train and test a knowledge base
Publish a knowledge base
Create a multi-turn conversation
Add alternate phrasing
Add chit-chat to a knowledge base
Export a knowledge base
Create a multi-language question answering solution

Implement knowledge mining and document intelligence solutions (10–15%)

Implement an Azure AI Search solution

Provision an Azure AI Search resource
Create data sources
Create an index
Define a skillset
Implement custom skills and include them in a skillset
Create and run an indexer
Query an index, including syntax, sorting, filtering, and wildcards
Manage Knowledge Store projections, including file, object, and table projections

Implement an Azure AI Document Intelligence solution

Provision a Document Intelligence resource
Use prebuilt models to extract data from documents
Implement a custom document intelligence model
Train, test, and publish a custom document intelligence model
Create a composed document intelligence model
Implement a document intelligence model as a custom Azure AI Search skill

Implement generative AI solutions (10–15%)

Use Azure OpenAI Service to generate content

Provision an Azure OpenAI Service resource
Select and deploy an Azure OpenAI model
Submit prompts to generate natural language
Submit prompts to generate code
Use the DALL-E model to generate images
Use Azure OpenAI APIs to submit prompts and receive responses

Optimize generative AI

Configure parameters to control generative behavior
Apply prompt engineering techniques to improve responses
Use your own data with an Azure OpenAI model
Fine-tune an Azure OpenAI model

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More courses

Azure Fundamentals,Azure AI Engineer Associate,Azure Security Engineer Associate,DevOps Engineer Expert,Azure Network Engineer Associate,Windows Server Hybrid Administrator Associate,

16 reviews for Azure AI Fundamentals

Abhishek

August 16, 2024 at 12:56 pm

I was able to achieve my AI-900 certification. Thank you very much to ALL ITprep for the help!

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