Use this quick start guide to collect all the information about Designing and Implementing a Microsoft Azure AI Solution (AI-102) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the AI-102 Designing and Implementing a Microsoft Azure AI Solution exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual Microsoft MCA Azure AI Engineer certification exam.
The Designing and Implementing a Microsoft Azure AI Solution certification is mainly targeted to those candidates who want to build their career in Microsoft Azure domain. The Microsoft Certified - Azure AI Engineer Associate exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of Designing and Implementing a Microsoft MCA Azure AI Engineer.
Designing and Implementing a Microsoft Azure AI Solution Exam Summary:
| Exam Name | Microsoft Certified - Azure AI Engineer Associate |
| Exam Code | AI-102 |
| Exam Price | $165 (USD) |
| Duration | 120 mins |
| Number of Questions | 40-60 |
| Passing Score | 700 / 1000 |
| Books / Training | AI-102T00-A: Designing and Implementing a Microsoft Azure AI Solution |
| Schedule Exam | Pearson VUE |
| Sample Questions | Designing and Implementing a Microsoft Azure AI Solution Sample Questions |
| Practice Exam | Microsoft AI-102 Certification Practice Exam |
Microsoft AI-102 Exam Syllabus Topics:
| Topic | Details |
|---|---|
Plan and Manage an Azure AI solution (20-25%) |
|
| Select the appropriate Azure AI Services |
- Select the appropriate service for a generative AI solution - 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 an information extraction 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 - Choose the appropriate AI models for your solution - Deploy AI models using the appropriate deployment options - Install and utilize the appropriate SDKs and APIs - 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 |
- Monitor an Azure AI resource - Manage costs for Azure AI services - Manage and protect account keys - Manage authentication for an Azure AI Service resource |
| Implement AI solutions responsibly |
- Implement content moderation solutions - Configure responsible AI insights, including content safety - Implement responsible AI, including content filters and blocklists - Prevent harmful behavior, including prompt shields and harm detection - Design a responsible AI governance framework |
Implement generative AI solutions (15-20%) |
|
| Build generative AI solutions with Azure AI Foundry |
- Plan and prepare for a generative AI solution - Deploy a hub, project, and necessary resources with Azure AI Foundry - Deploy the appropriate generative AI model for your use case - Implement a prompt flow solution - Implement a RAG pattern by grounding a model in your data - Evaluate models and flows - Integrate your project into an application with Azure AI Foundry SDK - Utilize prompt templates in your generative AI solution |
| Use Azure OpenAI Service to generate content |
- Provision an Azure OpenAI Service resource - Select and deploy an Azure OpenAI model - Submit prompts to generate code and natural language responses - Use the DALL-E model to generate images - Integrate Azure OpenAI into your own application - Use large multimodal models in Azure OpenAI - Implement an Azure OpenAI Assistant |
| Optimize and operationalize a generative AI solution |
- Configure parameters to control generative behavior - Configure model monitoring and diagnostic settings, including performance and resource consumption - Optimize and manage resources for deployment, including scalability and foundational model updates - Enable tracing and collect feedback - Implement model reflection - Deploy containers for use on local and edge devices - Implement orchestration of multiple generative AI models - Apply prompt engineering techniques to improve responses - Fine-tune an generative model |
Implement an agentic solution (5-10%) |
|
| Create custom agents |
- Understand the role and use cases of an agent - Configure the necessary resources to build an agent - Create an agent with the Azure AI Agent Service - Implement complex agents with Semantic Kernel and Autogen - Implement complex workflows including orchestration for a multi-agent solution, multiple users, and autonomous capabilities - Test, optimize and deploy an agent |
Implement computer vision solutions (10-15%) |
|
| 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 vision models |
- 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 - Build a custom vision model code first |
| 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 (15-20%) |
|
| Analyze and translate text |
- Extract key phrases and entities - Determine sentiment of text - Detect the language used in text - Detect personally identifiable information (PII) in text - Translate text and documents by using the Azure AI Translator service |
| Process and translate speech |
- Integrate generative AI speaking capabilities in an application - Implement text-to-speech and speech-to-text using Azure AI Speech - Improve text-to-speech by using Speech Synthesis Markup Language (SSML) - Implement custom speech solutions with Azure AI Speech - Implement intent and keyword recognition with Azure AI Speech - Translate speech-to-speech and speech-to-text by using the Azure AI Speech service |
| Implement custom language models |
- Create intents, entities, and add utterances - Train, evaluate, deploy, and test a language understanding model - Optimize, backup, and recover language understanding model - Consume a language model from a client application - Create a custom question answering project - Add question-and-answer pairs and import sources for question answering - Train, test, and publish a knowledge base - Create a multi-turn conversation - Add alternate phrasing and chit-chat to a knowledge base - Export a knowledge base - Create a multi-language question answering solution - Implement custom translation, including training, improving, and publishing a custom model |
Implement knowledge mining and information extraction solutions (15-20%) |
|
| Implement an Azure AI Search solution |
- Provision an Azure AI Search resource, create an index, and define a skillset - Create data sources and indexers - 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 semantic and vector store solutions |
| 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 |
| Extract information with Azure AI Content Understanding |
- Create an OCR pipeline to extract text from images and documents - Summarize, classify, and detect attributes of documents - Extract entities, tables, and images from documents - Process and ingest documents, images, videos, and audio with Azure AI Content Understanding |
To ensure success in Microsoft MCA Azure AI Engineer certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam.