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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

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

Map security requirements to tools, technologies, and processes

• Identify auditing requirements
• 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 appropriate tools for a solution

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

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

2. Design AI solutions (40-45%)

Design solutions that include one or more pipelines

• Design pipelines that use AI apps
• 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
• Define an AI application workflow process
• Design pipelines that call Azure Machine Learning models

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 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
• Ensure that data adheres to compliance requirements defined by your organization
• Define how users and applications will authenticate to AI services

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

Implement an AI workflow

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

Integrate AI services with solution components

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

Monitor and evaluate the AI environment

• Recommend changes to an AI solution based on performance data
• 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
• Identify the differences between expected and actual workflow throughput

How much AI-102: Designing and Implementing an Azure AI Solution Exam Cost

The price of the Microsoft Mobility and Devices Fundamentals exam is $165 USD, for more information related to exam price please visit to Microsoft Training website as prices of Microsoft exams fees get varied country wise.

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