The Microsoft AI-102J exam is intended for AI solution architects, AI developers, and data scientists who have experience in designing and implementing AI solutions on the Microsoft Azure platform. Candidates are expected to have a solid understanding of programming languages such as Python, R, and C#, as well as experience in designing and implementing AI solutions using these languages. AI-102J exam is a great way for professionals to demonstrate their skills and knowledge in AI solution design and implementation on the Azure platform, and to gain recognition as a Microsoft Certified Azure AI Engineer Associate.

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

Map security requirements to tools, technologies, and processes

• Identify which users and groups have access to information and interfaces
• Identify appropriate tools for a solution
• Identify auditing requirements
• 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 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 pipelines that use AI apps
• Select an AI solution that meet cost constraints
• Design a strategy for ingest and egress data
• Define an AI application workflow process
• Design the integration point between multiple workflows and pipelines
• 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 and AI solutions
• Integrate bots with Azure app services and Azure Application Insights
• Design bots that integrate with channels
• 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
• 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
• 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
• Develop AI pipelines
• Create solution endpoints
• Implement data logging processes
• Develop streaming solutions
• Define and construct interfaces for custom AI services

Integrate AI services with solution components

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

Monitor and evaluate the AI environment

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

To become certified in Microsoft AI-102, the candidate must demonstrate their ability to design and implement AI solutions using various Azure services such as Azure Cognitive Services, Azure Machine Learning, and Azure Bot Service. AI-102J exam consists of multiple-choice questions, case studies, and scenarios that require the candidate to apply their knowledge and skills to solve real-world problems.

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

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Microsoft AI-102 exam is ideal for individuals who are looking to advance their career in the field of AI and machine learning. Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版) certification is recognized globally and is highly valued by employers. Companies that use Azure services and tools for implementing AI solutions are always looking for individuals who have the knowledge and skills to design and implement these solutions.