Skills measured

• Implement Computer Vision solutions (20-25%)
• Plan and manage an Azure Cognitive Services solution (15-20%)
• Implement knowledge mining solutions (15-20%)
• Implement natural language processing solutions (20-25%)
• Implement conversational AI solutions (15-20%)

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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.

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

Map security requirements to tools, technologies, and processes

• Identify auditing requirements
• 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

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

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

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

• 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

• Design strategies to ensure that the solution meets data privacy regulations and industry standards
• 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
• 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
• Define and construct interfaces for custom AI services
• Create solution endpoints
• Develop AI pipelines
• Develop streaming solutions
• Implement data logging processes

Integrate AI services with solution components

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

Monitor and evaluate the AI environment

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

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

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.

New updates

In recent years, some changes are taking place in this line about the new points are being constantly tested in the Designing and Implementing a Microsoft Azure AI Solution real exam. So our experts highlight the new type of questions and add updates into the practice materials, and look for shifts closely when they take place. As to the rapid changes happened in this exam, experts will fix them and we assure your AI-102 exam simulation you are looking at now are the newest version. Materials trends are not always easy to forecast, but they have predictable pattern for them by ten-year experience who often accurately predict points of knowledge occurring in next AI-102 preparation materials: Designing and Implementing a Microsoft Azure AI Solution.