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Who should take the AI-102: Designing and Implementing an Azure AI Solution Exam

The AI-102 Exam certification is an internationally-recognized certification which help to have validation for Azure AI Solution Architects who have ability to accomplish the following technical tasks: analyze solution requirements; design solutions; integrate AI models into solutions; and deploy and manage solutions.

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

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Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution

Candidates for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution build, manage, and deploy AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.

Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, maintenance, performance tuning, and monitoring.

They work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end AI solutions.

Candidates for this exam should be proficient in C# or Python and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure.

They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principles.

Part of the requirements for: Microsoft Certified: Azure AI Engineer Associate

Download exam skills outline

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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

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

Map security requirements to tools, technologies, and processes

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

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

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

2. Design AI solutions (40-45%)

Design solutions that include one or more pipelines

• Design pipelines that use AI apps
• Define an AI application workflow process
• 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
• Select an AI solution that meet cost constraints

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

Design the compute infrastructure to support a solution

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

Design for data governance, compliance, integrity, and security

• Ensure that data adheres to compliance requirements defined by your organization
• Ensure appropriate governance of data
• Design a content moderation strategy for data usage within an AI solution
• 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

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

Integrate AI services with solution components

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

Monitor and evaluate the AI environment

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

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