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 (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 custom question answering solution by using Azure AI Language |
- Create a custom 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 - Use large multimodal models in Azure OpenAI |
| 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 |
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.