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Microsoft AI-102 Exam Syllabus Topics:

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

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Our product boosts both the high passing rate which is about 98%-100% and the high hit rate to have few difficulties to pass the test. Our AI-102 exam simulation is compiled based on the resources from the authorized experts' diligent working and the real exam and confer to the past years' exam papers thus they are very practical. The content of the questions and answers of AI-102 exam questions: Designing and Implementing a Microsoft Azure AI Solution is refined and focuses on the most important information. To let the clients be familiar with the atmosphere and pace of the real exam we provide the function of stimulating the exam. Our expert team updates the AI-102 study guide frequently to let the clients practice more. So the quality of our product is very high and we can guarantee to you that you will have few difficulties to pass the exam.

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 appropriate tools for a solution
• Identify auditing requirements

Select the software, services, and storage required to support a solution

• Identify appropriate services and tools for a solution
• Identify integration points with other Microsoft services
• 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 pipelines that use AI apps
• Define an AI application workflow process
• Design pipelines that call Azure Machine Learning models
• Design a strategy for ingest and egress data

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 bot services that use Language Understanding (LUIS)
• Design bots that integrate with channels
• 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
• Identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
• Select a compute solution that meets cost constraints

Design for data governance, compliance, integrity, and security

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

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

Implement an AI workflow

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

Integrate AI services with solution components

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

Monitor and evaluate the AI environment

• 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
• Maintain an AI solution for continuous improvement

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

Candidates for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution build, manage, and deploy AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.

Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, maintenance, performance tuning, and monitoring.

They work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end AI solutions.

Candidates for this exam should be proficient in C# or Python and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure.

They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principles.

Part of the requirements for: Microsoft Certified: Azure AI Engineer Associate

Download exam skills outline

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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