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Exam AI-102 contains five topics each of which is intended to check specific skills.
1. Plan and Manage an Azure Cognitive Services Solution
This topic implies your ability to choose the suitable Cognitive Services resource, create it, plan and design security for a Cognitive Services solution, and apply Cognitive Services containers. This means that you should be competent in selecting the appropriate cognitive service for solutions that refer to language analysis, speech, decision support, and vision. You also should possess skills to operate costs of Cognitive Services, create a Cognitive Services resource, and monitor a cognitive service. This part also checks how well you can operate Cognitive Services account keys, and protect Cognitive Services. Your knowledge of using Face API, Computer Vision, Speech, Text Analysis, and ability to integrate Cognitive Services Containers in Microsoft Azure will also be assessed.
2. Implement Computer Vision Solutions
The second topic is designed to check your skills in using the Computer Vision API to get image descriptions, define landmarks, find brands, edit content in images, and create thumbnails. In this part, you are expected to be able to detect faces and recognize them in images, analyze facial features, and match similar faces with the help of the Face API. Being competent in utilizing the Custom Vision service, you should demonstrate your skills in applying image classification and implementing an object detection solution. Besides, your ability to analyze video by implementing Azure Video Analyzer for Media will be measured.
3. Implement Natural Language Processing Solutions
In the third topic, candidates are required to show their skills in analyzing text by utilizing the Text Analytics service, control speech by implementing the Speech service, translate the text with the help of the Translator service. This domain also checks your proficiency in creating and optimizing an initial language model by utilizing LUIS, and finally, managing it.
4. Implement Knowledge Mining Solutions
In this domain, you will be required to have expertise related to applying a Cognitive Search solution, which implies creating data sources, identifying an index, running an indexer, and using synonyms. This topic also aims to evaluate your ability to apply an enrichment pipeline, use a knowledge store, operate a Cognitive Search solution and indexing.
5. Implement Conversational AI Solutions
This domain will evaluate your capacity in utilizing QnA Maker to make a knowledge base, creating and implementing conversation flow, creating a bot by utilizing either the Bot Framework Composer or the Bot Framework SDK. Finally, you will need to demonstrate your skills in integrating Cognitive Services into a bot.
Before enrolling in the process of taking the AI-102 exams, candidates should be skilled in implementing Python and C#, using APIs and SDKs based on REST to create natural language processing solutions, computer vision solutions, and knowledge mining and communicative AI solutions based on Azure. In addition, such specialists should be knowledgeable of the elements that create the Azure AI portfolio as well as the data storage options. To add more, they should be able to implement AI principles appropriately.
| Number of Questions | 40-60 |
| Sample Questions | Designing and Implementing a Microsoft Azure AI Solution Sample Questions |
| Duration | 130 mins |
| Schedule Exam | Pearson VUE |
| Exam Name | Microsoft Certified - Azure AI Engineer Associate |
| Exam Code | AI-102 |
| Exam Price | $165 (USD) |
| Passing Score | 700 / 1000 |
| Books / Training | Course AI-102T00: Designing and Implementing a Microsoft Azure AI Solution |
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10-15% | - Analyze images and detect objects/features - Process and index video content - Integrate vision capabilities into applications - Extract text and handwriting from images - Build and deploy custom vision models |
| Topic 2: Implement knowledge mining and information extraction solutions | 15-20% | - Ingest and process structured/unstructured data - Build knowledge bases and search indexes - Implement intelligent search and retrieval - Extract entities, relationships, and key phrases |
| Topic 3: Plan and manage an Azure AI solution | 20-25% | - Select appropriate Microsoft Foundry Services - Select suitable AI models - Create and configure Azure AI resources - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Plan solutions aligned with responsible AI principles - Monitor, optimize, and secure AI solutions |
| Topic 4: Implement natural language processing solutions | 15-20% | - Build conversational AI and chatbots - Customize and deploy NLP models - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection |
| Topic 5: Implement generative AI solutions | 15-20% | - Apply prompt engineering and fine-tuning - Integrate Azure OpenAI and other generative models - Orchestrate multiple models and containers - Deploy and manage generative models - Implement model monitoring and feedback |
| Topic 6: Implement an agentic solution | 5-10% | - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types |
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