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Designing and Implementing a Microsoft Azure AI Solution-AI 102

“Developing Solutions for Microsoft Azure,” is part of the Microsoft Certified: Azure Developer Associate certification track. This exam is designed to evaluate a candidate’s ability to design, develop, and deploy applications on the Microsoft Azure cloud platform.Earning the Microsoft Certified: Azure Developer Associate certification demonstrates your expertise in developing cloud solutions on Azure. It can enhance your career prospects, particularly if you’re looking for roles related to cloud application development and Azure-based projects.

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The Syllabus

Curriculum Designed by Experts

Plan and Manage an Azure AI Solution

Select the appropriate Azure AI Service

  • Select the appropriate service for a vision solution
  • Select the appropriate service for a language analysis solution
  • Select the appropriate service for a decision support solution
  • Select the appropriate service for a speech solution
  • Select the appropriate Applied AI services

Plan and configure security for Azure AI Services

  • Manage account keys
  • Manage authentication for a resource
  • Secure services by using Azure Virtual Networks
  • Plan for a solution that meets responsible AI principles

Create and manage an Azure AI service

  • Create an Azure AI resource
  • Configure diagnostic logging
  • Manage costs for Azure AI services
  • Monitor an Azure AI resource

Deploy Azure AI services

  • Determine a default endpoint for a service
  • Create a resource by using the Azure portal
  • Integrate Azure AI services into a continuous integration/continuous deployment (CI/CD) pipeline
  • Plan a container deployment
  • Implement prebuilt containers in a connected environment

Create solutions to detect anomalies and improve content

  • Create a solution that uses Anomaly Detector, part of Cognitive Services
  • Create a solution that uses Azure Content Moderator, part of Cognitive Services
  • Create a solution that uses Personalizer, part of Cognitive Services
  • Create a solution that uses Azure Metrics Advisor, part of Azure Applied AI Services
  • Create a solution that uses Azure Immersive Reader, part of Azure Applied AI Services
Implement image and video processing solutions

Analyze images

  • Select appropriate visual features to meet image processing requirements
  • Create an image processing request to include appropriate image analysis features
  • Interpret image processing responses

Extract text from images

  • Extract text from images or PDFs by using the Computer Vision service
  • Convert handwritten text by using the Computer Vision service
  • Extract information using prebuilt models in Azure Form Recognizer
  • Build and optimize a custom model for Azure Form Recognizer

Implement image classification and object detection by using the Custom Vision service, part of Azure Cognitive Services

  • Choose between image classification and object detection models
  • Specify model configuration options, including category, version, and compact
  • Label images
  • Train custom image models, including image classification and object detection
  • Manage training iterations
  • Evaluate model metrics
  • Publish a trained model
  • Export a model to run on a specific target
  • Implement a Custom Vision model as a Docker container
  • Interpret model responses

Process videos

  • Process a video by using Azure Video Indexer
  • Extract insights from a video or live stream by using Azure Video Indexer
  • Implement content moderation by using Azure Video Indexer
  • Integrate a custom language model into Azure Video Indexer
Implement Natural Language Processing Solutions

Analyze text

  • Retrieve and process key phrases
  • Retrieve and process entities
  • Retrieve and process sentiment
  • Detect the language used in text
  • Detect personally identifiable information (PII)

Process speech

  • Implement and customize text-to-speech
  • Implement and customize speech-to-text
  • Improve text-to-speech by using SSML and Custom Neural Voice
  • Improve speech-to-text by using phrase lists and Custom Speech
  • Implement intent recognition
  • Implement keyword recognition

Translate language

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

Build and manage a language understanding model

  • Create intents and add utterances
  • Create entities
  • Train evaluate, deploy, and test a language understanding model
  • Optimize a Language Understanding (LUIS) model
  • Integrate multiple language service models by using an orchestration workflow
  • Import and export language understanding models

Create a question answering solution

  • 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
  • Create a multi-domain question answering solution
  • Use metadata for question-and-answer pairs
Implement Knowledge Mining Solutions

Implement a Cognitive Search solution

  • Provision a Cognitive Search resource
  • Create data sources
  • Define an index
  • Create and run an indexer
  • Query an index, including syntax, sorting, filtering, and wildcards
  • Manage knowledge store projections, including file, object, and table projections

Apply AI enrichment skills to an indexer pipeline

  • Attach a Cognitive Services account to a skillset
  • Select and include built-in skills for documents
  • Implement custom skills and include them in a skillset
  • Implement incremental enrichment
Implement Conversational AI Solutions

Design and implement conversation flow

  • Design conversational logic for a bot
  • Choose appropriate activity handlers, dialogs or topics, triggers, and state handling for a bot

Build a conversational bot

  • Create a bot from a template
  • Create a bot from scratch
  • Implement activity handlers, dialogs or topics, and triggers
  • Implement channel-specific logic
  • Implement Adaptive Cards
  • Implement multi-language support in a bot
  • Implement multi-step conversations
  • Manage state for a bot
  • Integrate Cognitive Services into a bot, including question answering, language understanding, and Speech service

Test, publish, and maintain a conversational bot

  • Test a bot using the Bot Framework Emulator or the Power Virtual Agents web app
  • Test a bot in a channel-specific environment
  • Troubleshoot a conversational bot
  • Deploy bot logic

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    Red Hat Linux Course Projects

    Infrastructure Provisioning

    And Configuration Management

    Implementing automated infrastructure provisioning and configuration management using Ansible. This may include setting up servers, networking devices, and other infrastructure components using playbooks and roles. 

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    Applications Deployment

    And Orchestration

    Automating the deployment and orchestration of applications across development, testing, and production environments. This could involve deploying web servers, databases. middleware, and other application components using Ansible

    Continuous Integration

    And Continuous Deployment

    Integrating Ansible into CI/CD pipelines to automate software. build, test, and deployment processes. This may include automating the creation of build artifacts, running tests, and deploying applications to various environments.