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

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

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

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

Implement an AI workflow

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

Integrate AI services with solution components

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

Monitor and evaluate the AI environment

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

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.

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.

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