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Prerequisites

Before enrolling in the process of taking the AI-102 exams, candidates should be skilled in implementing Python and C#, using APIs and SDKs based on REST to create natural language processing solutions, computer vision solutions, and knowledge mining and communicative AI solutions based on Azure. In addition, such specialists should be knowledgeable of the elements that create the Azure AI portfolio as well as the data storage options. To add more, they should be able to implement AI principles appropriately.

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

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

Map security requirements to tools, technologies, and processes

• Identify appropriate tools for a solution
• 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

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
• 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 the integration point between multiple workflows and pipelines

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 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

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

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

Implement an AI workflow

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

Integrate AI services with solution components

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

Monitor and evaluate the AI environment

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

Microsoft AI-102日本語 Exam Syllabus Topics: