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Exam Details

Speaking of the exam details, the test will contain from 40 to 60 questions of various types which you need to complete within 100 or 120 minutes (depends on the inclusion of labs). To pass the exam you need to schedule the test on the PearsonVUE platform, pay an exam fee which is currently $165, and score at least 700 points or more out of 1000. And, of course, the knowledge of exam topics is a must.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102

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Topics Covered

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.

Designing and Implementing a Microsoft Azure AI Solution Exam Certification Details:

Books / TrainingCourse AI-102T00: Designing and Implementing a Microsoft Azure AI Solution
Exam CodeAI-102
Exam NameMicrosoft Certified - Azure AI Engineer Associate
Exam Price$165 (USD)
Number of Questions40-60
Duration130 mins
Sample QuestionsDesigning and Implementing a Microsoft Azure AI Solution Sample Questions
Passing Score700 / 1000
Schedule ExamPearson VUE

Microsoft AI-102 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implement generative AI solutions15-20%- Deploy and manage generative models
- Integrate Azure OpenAI and other generative models
- Implement model monitoring and feedback
- Apply prompt engineering and fine-tuning
- Orchestrate multiple models and containers
Topic 2: Implement natural language processing solutions15-20%- Perform text analysis, sentiment detection, and language detection
- Build conversational AI and chatbots
- Implement translation and summarization
- Customize and deploy NLP models
Topic 3: Plan and manage an Azure AI solution20-25%- Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining
- Plan solutions aligned with responsible AI principles
- Select appropriate Microsoft Foundry Services
- Create and configure Azure AI resources
- Select suitable AI models
- Monitor, optimize, and secure AI solutions
Topic 4: Implement computer vision solutions10-15%- Process and index video content
- Integrate vision capabilities into applications
- Analyze images and detect objects/features
- Extract text and handwriting from images
- Build and deploy custom vision models
Topic 5: Implement knowledge mining and information extraction solutions15-20%- Extract entities, relationships, and key phrases
- Ingest and process structured/unstructured data
- Implement intelligent search and retrieval
- Build knowledge bases and search indexes
Topic 6: Implement an agentic solution5-10%- 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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