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Introduction to AI-102: Designing and Implementing an Azure AI Solution Exam

Candidates for AI-102 Exam are seeking to prove fundamental knowledge and skills in Designing and Implementing an Azure AI Solution domain. Before taking this exam, aspirants ought to have a solid fundamental information of the concepts shared in preparation guide as well as basic understanding of Azure administration, Azure development, and DevOpss would give an added edge.

This exam validates the ability to use the various services within the Microsoft Azure Artificial Intelligence (AI) portfolio.

It is suggested that professionals accustomed to the ideas and also the technologies represented here by taking relevant training courses. Candidates are expected to have some hands-on experience on bot services that use Language Understanding , bots with Azure Application Insights, creating a GPU, FPGA, or CPU-based solution, implementing AI workflow.

After passing this exam, candidates get a certificate from Microsoft that helps them to demonstrate their proficiency to their clients and employers.

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.

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

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

Map security requirements to tools, technologies, and processes

• Identify appropriate tools for a solution
• Identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements
• Identify auditing requirements
• Identify which users and groups have access to information and interfaces

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

2. Design AI solutions (40-45%)

Design solutions that include one or more pipelines

• Design a strategy for ingest and egress data
• Select an AI solution that meet cost constraints
• Design pipelines that use AI apps
• 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
• 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
• 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

• 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
• Ensure appropriate governance of data

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

Implement an AI workflow

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

Integrate AI services with solution components

• Configure integration with Cognitive Services
• Configure prerequisite components and input datasets to allow the consumption of Cognitive Services APIs
• Implement Azure Search in a solution
• 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 KPIs, reported metrics, and root causes of the differences
• Recommend changes to an AI solution based on performance data
• Identify the differences between expected and actual workflow throughput
• Monitor AI components for availability

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