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How to book the AI-102: Designing and Implementing an Azure AI Solution Exam

These are following steps for registering the AI-102: Designing and Implementing an Azure AI Solution exam.

• Step 1: Visit to Microsoft Learning and search for AI-102: Developing Solutions for Microsoft Azure.
• Step 2: Sign up/Login to Pearson VUE account
• Step 3: Select local centre based on your country, date, time and confirm with a payment method.

What is the duration, language, and format of AI-102: Designing and Implementing an Azure AI Solution Exam

• Type of Questions: This test format is multiple choice.
• Length of Examination: 50 mins
• Passing Score: 700 / 1000
• Language: English, Japanese, Chinese (Simplified), Korean.
• This is beta exam.
• Number of Questions: 40 to 60 questions(Since Microsoft does not publish this information, the number of exam questions may change without notice.)

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

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 processing architecture for a solution
• Select the appropriate AI models and services
• Identify automation requirements
• Identify components and technologies required to connect service endpoints
• 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 pipelines that use AI apps
• Design pipelines that call Azure Machine Learning models
• Design the integration point between multiple workflows and pipelines
• Design a strategy for ingest and egress data
• Select an AI solution that meet cost constraints
• Define an AI application workflow process

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

• Design bot services that use Language Understanding (LUIS)
• Integrate bots and AI solutions
• Design bots that integrate with channels
• Integrate bots with Azure app services and Azure Application Insights

Design the compute infrastructure to support a solution

• Select a compute solution that meets cost constraints
• Identify whether to create a GPU, FPGA, or CPU-based solution
• 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 strategies to ensure that the solution meets data privacy regulations and industry standards
• Define how users and applications will authenticate to AI services
• Ensure that data adheres to compliance requirements defined by your organization
• Design a content moderation strategy for data usage within an AI solution

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

Implement an AI workflow

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

Integrate AI services with solution components

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

Monitor and evaluate the AI environment

• Identify the differences between KPIs, reported metrics, and root causes of the differences
• 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 expected and actual workflow throughput

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