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

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This exam measures your ability to accomplish the following technical tasks: plan and manage an Azure Cognitive Services solutions; implement Computer Vision solutions; implement natural language processing solutions; implement knowledge mining solutions; and implement conversational AI solutions.

Introduction to AI-102: Designing and Implementing an Azure AI Solution Exam

Candidates for AI-102 Exam are seeking to prove fundamental knowledge and skills in Designing and Implementing an Azure AI Solution domain. Before taking this exam, aspirants ought to have a solid fundamental information of the concepts shared in preparation guide as well as basic understanding of Azure administration, Azure development, and DevOpss would give an added edge.

This exam validates the ability to use the various services within the Microsoft Azure Artificial Intelligence (AI) portfolio.

It is suggested that professionals accustomed to the ideas and also the technologies represented here by taking relevant training courses. Candidates are expected to have some hands-on experience on bot services that use Language Understanding , bots with Azure Application Insights, creating a GPU, FPGA, or CPU-based solution, implementing AI workflow.

After passing this exam, candidates get a certificate from Microsoft that helps them to demonstrate their proficiency to their clients and employers.

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

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

Map security requirements to tools, technologies, and processes

• Identify which users and groups have access to information and interfaces
• Identify auditing requirements
• 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 appropriate services and tools for a solution
• Identify storage required to store logging, bot state data, and Cognitive Services output
• Identify integration points with other Microsoft services

2. Design AI solutions (40-45%)

Design solutions that include one or more pipelines

• Design the integration point between multiple workflows and pipelines
• Define an AI application workflow process
• Select an AI solution that meet cost constraints
• Design pipelines that use AI apps
• Design a strategy for ingest and egress data
• 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 and AI solutions
• Design bots that integrate with channels
• Design bot services that use Language Understanding (LUIS)
• Integrate bots with Azure app services and Azure Application Insights

Design the compute infrastructure to support a solution

• Identify whether to create a GPU, FPGA, or CPU-based solution
• Select a compute solution that meets cost constraints
• 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
• Design strategies to ensure that the solution meets data privacy regulations and industry standards
• Design a content moderation strategy for data usage within an AI solution
• Define how users and applications will authenticate to AI services
• Ensure appropriate governance of data

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

Implement an AI workflow

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

Integrate AI services with solution components

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

Monitor and evaluate the AI environment

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

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For candidates that are aiming to develop their skills in building, operating, and deploying AI solutions with the help of such services as Azure Applied AI services and Azure Cognitive Services, the best variant is to pass the Microsoft AI-102 exam. This exam is all about designing and applying a Microsoft Azure AI Solution, and leads to getting the Microsoft Certified: Azure AI Engineer Associate certification.

Passing this exam implies that certified candidates are able to participate in all stages of AI solutions development from defining requirements to performance tuning and monitoring. These professionals cooperate with solution architects, as well as with data engineers and scientists, AI developers to show their vision and create comprehensive AI solutions.

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