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

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

Map security requirements to tools, technologies, and processes

• 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
• Identify appropriate tools for a solution

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 appropriate services and tools for a solution
• Identify integration points with other Microsoft services

2. Design AI solutions (40-45%)

Design solutions that include one or more pipelines

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

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
• Integrate bots and AI solutions
• Design bot services that use Language Understanding (LUIS)

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 appropriate governance of data
• Design a content moderation strategy for data usage within an AI solution
• Define how users and applications will authenticate to AI services
• 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

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

Implement an AI workflow

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

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

• 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
• Identify the differences between KPIs, reported metrics, and root causes of the differences
• Monitor AI components for availability

For more info read reference:

microsoft learning site AI-102 Skills measured Publish a Machine Learning Experiment with Microsoft Azure Machine Learning Studio Process and translate speech with Azure Cognitive Speech Services

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