How to Register For Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution?

Exam Register Link: https://examregistration.microsoft.com/?locale=en-us&examcode=AI-102&examname=Exam%20AI-102:%20Designing%20and%20Implementing%20a%20Microsoft%20Azure%20AI%20Solution&returnToLearningUrl=https%3A%2F%2Fdocs.microsoft.com%2Flearn%2Fcertifications%2Fexams%2Fai-102

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

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Topics of AI-102: Designing and Implementing an Azure AI Solution Exam

Candidates should apprehend the examination topics before they begin of preparation. because it'll extremely facilitate them in touch the core. Our AI-102 exam dumps will include the following topics:

1. Analyze solution requirements (25-30%)

Recommend Cognitive Services APIs to meet business requirements

Select the appropriate data processing technologies
Select the appropriate AI models and services
Identify automation requirements
Select the processing architecture for a solution
Identify components and technologies required to connect service endpoints

Map security requirements to tools, technologies, and processes

Identify which users and groups have access to information and interfaces
Identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements
Identify auditing requirements
Identify appropriate tools for a solution

Select the software, services, and storage required to support a solution

Identify storage required to store logging, bot state data, and Cognitive Services output
Identify integration points with other Microsoft services
Identify appropriate services and tools for a solution

2. Design AI solutions (40-45%)

Design solutions that include one or more pipelines

Define an AI application workflow process
Select an AI solution that meet cost constraints
Design pipelines that use AI apps
Design pipelines that call Azure Machine Learning models
Design the integration point between multiple workflows and pipelines
Design a strategy for ingest and egress data

Design solutions that uses Cognitive Services

Design solutions that use vision, speech, language, knowledge, search, and anomaly detection APIs

Design solutions that implement the Bot Framework

Integrate bots and AI solutions
Design bots that integrate with channels
Integrate bots with Azure app services and Azure Application Insights
Design bot services that use Language Understanding (LUIS)

Design the compute infrastructure to support a solution

Identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
Select a compute solution that meets cost constraints
Identify whether to create a GPU, FPGA, or CPU-based solution

Design for data governance, compliance, integrity, and security

Ensure appropriate governance of data
Design a content moderation strategy for data usage within an AI solution
Ensure that data adheres to compliance requirements defined by your organization
Design strategies to ensure that the solution meets data privacy regulations and industry standards
Define how users and applications will authenticate to AI services

3. Implement and monitor AI solutions (25-30%)

Implement an AI workflow

Define and construct interfaces for custom AI services
Manage the flow of data through the solution components
Implement data logging processes
Develop AI pipelines
Develop streaming solutions
Create solution endpoints

Integrate AI services with solution components

Implement Azure Search in a solution
Configure integration with Cognitive Services
Configure prerequisite components to allow connectivity to the Bot Framework
Configure prerequisite components and input datasets to allow the consumption of Cognitive Services APIs

Monitor and evaluate the AI environment

Identify the differences between expected and actual workflow throughput
Identify the differences between KPIs, reported metrics, and root causes of the differences
Maintain an AI solution for continuous improvement
Monitor AI components for availability
Recommend changes to an AI solution based on performance data

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Microsoft AI-102 Exam Syllabus Topics:

Section	Weight	Objectives
Implement computer vision solutions	10-15%	- Process and index video content - Build and deploy custom vision models - Analyze images and detect objects/features - Integrate vision capabilities into applications - Extract text and handwriting from images
Implement knowledge mining and information extraction solutions	15-20%	- Implement intelligent search and retrieval - Build knowledge bases and search indexes - Ingest and process structured/unstructured data - Extract entities, relationships, and key phrases
Implement generative AI solutions	15-20%	- Integrate Azure OpenAI and other generative models - Orchestrate multiple models and containers - Deploy and manage generative models - Implement model monitoring and feedback - Apply prompt engineering and fine-tuning
Implement an agentic solution	5-10%	- Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration - Understand agent use cases and types
Plan and manage an Azure AI solution	20-25%	- Create and configure Azure AI resources - Select suitable AI models - Monitor, optimize, and secure AI solutions - 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
Implement natural language processing solutions	15-20%	- Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models - Implement translation and summarization - Build conversational AI and chatbots