AI-100: Designing and Implementing an Azure AI Solution


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 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

  • 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 for data
  • design strategies to ensure the solution meets data privacy and industry standard regulations

Implement and monitor AI solutions (25-30%)

Implement an AI workflow

  • develop AI pipelines
  • manage the flow of data through solution components
  • implement data logging processes
  • define and construct interfaces for custom AI services
  • integrate AI models with other solution components
  • design solution endpoints
  • develop streaming solutions

Integrate AI services with solution components

  • configure prerequisite components and input datasets to allow consumption of Cognitive Services APIs
  • configure integration with Azure Services
  • configure prerequisite components to allow connectivity with Bot Framework
  • implement 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 the AI solution for continuous improvement
  • monitor AI components for availability
  • recommend changes to an AI solution based on performance data


  • Azure Machine Learning
  • Azure Machine Learning Models
  • AI Models
  • AI Services
  • Language Understanding (LUIS)
  • GPU-based Solution
  • FPGA-based Solution
  • CPU-based Solution
  • AI Pipelines
  • Cognitive Services
  • Bot Framework
  • Bot Service
  • QnA Maker
  • Azure Search