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How to Prepare For AI-102: Designing and Implementing an Azure AI Solution Exam

Preparation Guide for AI-102: Designing and Implementing an Azure AI Solution Exam

Introduction

Microsoft has created a track for Azure professionals analyze the requirements for AI solutions, recommend appropriate tools and technologies, and implements solutions that meet scalability and performance requirements, to get certified this platform. solution architects translate the vision and work with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end solutions. The assessment is based on a rigorous exam using industry standard methodology to determine whether a candidate meets Microsoft's proficiency standards.

This certification been actually designed for aspirants design and implement AI apps and agents that use Microsoft Azure Cognitive Services.

For this exam candidate having proficiency in using cognitive service APIs to meet business requirements, appropriate AI models and services, automation requirements, data privacy and protection , bot state data , cognitive service output would be an added advantage.

Certification is evidence of your skills, expertise in those areas in which you like to work. If candidate wants to work as AI solution architect and prove his knowledge, certification offered by Microsoft. This AI-102 Exam Certification helps a candidate to validates his skills in Azure platform.

In this guide, we will cover the AI-102: Designing and Implementing an Azure AI Solution Certification exam, AI-102: Designing and Implementing an Azure AI Solution Certified professional salary and all aspects of the AI-102: Designing and Implementing an Azure AI Solution Certification.

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.

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Microsoft AI-102 Exam Syllabus Topics:

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

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