Designing and Implementing a Microsoft Azure AI Solution (AI-102) Practice Exam

The Designing and Implementing a Microsoft Azure AI Solution (AI-102) certification validates your ability to design, develop, and deploy artificial intelligence (AI) solutions on the Microsoft Azure platform. This globally recognized credential demonstrates your expertise in:

Who should consider this exam?

• Software developers: Enhance your skillset by Learninging to build AI-infused applications leveraging Azure AI services.
• Data scientists and machine Learninging engineers: Broaden your knowledge of deploying models into production environments.
• Cloud architects: Design and implement secure and scalable AI solutions on Azure.
• IT professionals seeking career advancement in AI: Validate your skills and stand out in the job market.

Key Roles and Responsibilities

• Plan and manage your AI solution lifecycle: Define project requirements, select appropriate Azure services, and manage the development and deployment process.
• Implement solutions for different AI use cases: Utilize Azure services for computer vision, natural language processing, knowledge mining, conversational AI, and more.
• Deploy and integrate AI models: Choose deployment options, manage model versions, and integrate models with applications.
• Monitor and optimize AI solutions: Assess model performance, identify areas for improvement, and maintain solution health.

Exam Details:

• Format: Performance-based exam with hands-on tasks and scenarios (90 minutes)
• Languages: English, Japanese, Chinese (Simplified), Korean (other languages offered periodically)
  
• Passing Score: 700

Microsoft Azure AI Solution AI-102 Course Outline

Module 1 - Describe Plan and manage an Azure AI solution (15–20%)

1.1 Explain selecting the appropriate Azure AI service

• Selecting the appropriate service for a computer vision solution
• Selecting the appropriate service for a natural language processing solution
• Selecting the appropriate service for a decision support solution
• Selecting the appropriate service for a speech solution
• Selecting the appropriate service for a generative AI solution
• Selecting the appropriate service for a document intelligence solution
• Selecting the appropriate service for a knowledge mining solution

1.2 Explain Planning, creating and deploying an Azure AI service

• Learning to plan for a solution that meets Responsible AI principles
• Learning to create an Azure AI resource
• Learning to determine a default endpoint for a service
• Learning to integrate Azure AI services into a continuous integration and continuous delivery (CI/CD) pipeline
• Learning to plan and implement a container deployment

1.3 Explain managing, monitoring and securing an Azure AI service

• Learning to configure diagnostic logging
• Learning to monitor an Azure AI resource
• Learning to manage costs for Azure AI services
• Learning to manage account keys
• Learning to protect account keys by using Azure Key Vault
• Learning to manage authentication for an Azure AI Service resource
• Learning to manage private communications

Module 2 - Describe implementing decision support solutions (10–15%)

2.1 Explain creating decision support solutions for data monitoring and anomaly detection

• Learning to implement a univariate anomaly detection solution with Azure AI Anomaly Detector
• Learning to implement a multivariate anomaly detection solution Azure AI Anomaly Detector
• Learning to implement a data monitoring solution with Azure AI Metrics Advisor

2.2 Explain creating decision support solutions for content delivery

• Learning to implement a text moderation solution with Azure AI Content Safety
• Learning to implement an image moderation solution with Azure AI Content Safety
• Implement a content personalization solution with Azure AI Personalizer

Module 3 - Implement computer vision solutions (15–20%)

3.1 Explain analyzing images

• Learning to select visual features to meet image processing requirements
• Learning to detect objects in images and generate image tags
• Learning to include image analysis features in an image processing request
• Learning to interpret image processing responses
• Learning to extract text from images using Azure AI Vision
• Learning to convert handwritten text using Azure AI Vision

3.3 Explain implementing custom computer vision models by using Azure AI Vision

• Learning to choose between image classification and object detection models
• Learning about Label images
• Learning to train a custom image model, including image classification and object detection
• Learning to evaluate custom vision model metrics
• Learning to publish a custom vision model
• Learning to consume a custom vision model

3.4 Explain analyzing videos

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

Module 4 - Describe implementing Natural Language Processing (NLP) solutions (30–35%)

4.1 Explain to analyze text by using Azure AI Language

• Learning to extract key phrases
• Learning to extract entities
• Learning to determine sentiment of text
• Learning to detect the language used in text
• Learning to detect personally identifiable information (PII) in text

4.2 Explain Process speech by using Azure AI Speech

• Learning to 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

4.3 Explain to translate language

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

4.4 Explain to implementing and managing a language understanding model by using Azure AI Language

• Learning to create intents and add utterances
• Learning to create entities
• Learning to train, evaluate, deploy, and test a language understanding model
• Learning to optimize a language understanding model
• Learning to consume a language model from a client application
• Learning to backup and recover language understanding models

4.5 Explain creating a question answering solution by using Azure AI Language

• Learning to create a question answering project
• Learning to add question-and-answer pairs manually
• Learning to import sources
• Learning to train and test a knowledge base
• Learning to publish a knowledge base
• Learning to create a multi-turn conversation
• Learning to add alternate phrasing
• Learning to add chit-chat to a knowledge base
• Learning to export a knowledge base
• Learning to create a multi-language question answering solution

Domain 5 -  Describe implementing knowledge mining and document intelligence solutions (10–15%)

5.1 Explain implementing an Azure Cognitive Search solution

• Learning to provision a Cognitive Search resource
• Learning to create data sources
• Learning to create an index
• Learning to define a skillset
• Learning to implement custom skills and include them in a skillset
• Learning to create and run an indexer
• Learning to query an index, including syntax, sorting, filtering, and wildcards
• Learning to manage Knowledge Store projections, including file, object, and table projections

5.2 Explain implementing an Azure AI Document Intelligence solution

• Learning to Provision a Document Intelligence resource
• Learning to use prebuilt models to extract data from documents
• Learning to implement a custom document intelligence model
• Learning to train, test, and publish a custom document intelligence model
• Learning to create a composed document intelligence model
• Learning to implement a document intelligence model as a custom Azure Cognitive Search skill

Domain 6 - Describe implementing generative AI solutions (10–15%)

6.1 Explain using Azure OpenAI Service to generate content

• Learning to provision an Azure OpenAI Service resource
• Learning to select and deploy an Azure OpenAI model
• Learning to submit prompts to generate natural language
• Learning to submit prompts to generate code
• Learning to use the DALL-E model to generate images
• Learning to use Azure OpenAI APIs to submit prompts and receive responses

6.2 Explain Optimize generative AI

• Learning to configure parameters to control generative behavior
• Learning to apply prompt engineering techniques to improve responses
• Learning to use your own data with an Azure OpenAI model
• Learning to fine-tune an Azure OpenAI model