Designing and Implementing an Azure AI Solution (AI-100) Practice Exam

Designing and Implementing an Azure AI Solution (AI-100)

About Designing and Implementing an Azure AI Solution (AI-100)

AI-100 exam is for candidates who analyze the requirements for AI solution with recommended tools and technologies and implements solutions that meet scalability and performance requirements. Also, candidates should know how to translate the vision from solution architects and work with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end solutions.

Skills Required for the Exam

This exam is best suitable for,

• Candidates have an understanding of designing and implementing the various AI applications using the Microsoft Azure Cognitive and Azure Bot service.
• Candidates should have knowledge of the components that makes up the Azure AI portfolio and the available data storage options.
• Candidates must have an understanding of implementing the AI solutions using Cognitive Services, Azure bots, Azure Search, and data storage in Azure. 
• Candidates must understand when a custom API should be developed to meet specific requirements.

Exam Pattern 

• Exam Name: Designing and Implementing an Azure AI Solution
• Exam Code: AI-100
  
• Number of Questions: 40-60
• Length of Time:  180 Minutes
• Registration Fee: $165.00
• Passing score: 700 (on a scale of 1-1000)
• Exam Language English, Japanese, Chinese, Korean

The Microsoft Azure AI Solution (AI-100) will Retire in June - Replaced by AI-102

Course Outline

The Designing and Implementing an Azure AI Solution (AI-100) Exam Covers the latest exam updates - 

Analyze solution requirements (25-30%) 

Recommend Cognitive Services APIs to meet business requirements

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

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

Design AI solutions (40-45%) 

Design solutions that include one or more pipelines 

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

Design solutions that use 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 

• 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
• Ensure appropriate governance of data 
• Design strategies to ensure that the solution meets data privacy regulations and industry standards

Implement and monitor AI solutions (25-30%) 

Implement an AI workflow 

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

Integrate AI services with solution components

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

Monitor and evaluate the AI environment 

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

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