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Exam AI-100: Designing and Implementing an Azure AI Solution

Candidates for this exam analyze requirements for AI cloud-based and hybrid AI solutions, recommends appropriate tools and technologies, and implements solutions that meet scalability and performance... requirements.

Candidates are aware of the various components that make up the Microsoft Azure AI portfolio, related open source frameworks and technologies, and available data storage options. Candidates use their understanding of cost models, capacity, and best practices to architect and implement AI solutions.

Candidates should have a working knowledge of basic statistics, data ethics, and data privacy.

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Exam AI-100: Designing and Implementing an Azure AI Solution

This exam measures your ability to accomplish the following technical tasks: analyze solution requirements; design solutions; integrate AI models into solutions; and deploy and manage solutions.

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Skills measured

Analyze solution requirements (20-25%)

Identify storage solutions

  • identify the appropriate storage capacity, storage types and storage locations for a solution
  • determine the storage technologies that the solution should use
  • identify the appropriate storage architecture for the solution
  • identify components and technologies required to connect data

Recommend tools, technologies, and processes to meet process flow 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

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

Select software and services required to support the solution

  • identify appropriate services/tools for the solution
  • identify integration points with other Microsoft services

Design solutions (30-35%)

Design an AI solution that includes one or more pipelines

  • define a workflow process
  • design a strategy for ingesting data

Design the compute infrastructure to support a solution

  • define infrastructure types
  • determine whether to create a GPU-based or CPU-based solution

Design Intelligent Edge solutions

  • identify appropriate tools for a solution
  • design solutions that incorporate AI pipeline components on Edge devices

Design data governance

  • design authentication architecture
  • design a content moderation strategy
  • ensure appropriate governance for data
  • design strategies to ensure the solution meets data privacy and industry standard regulations

Design solutions that adhere to cost constraints

  • choose a cost-effective data topology
  • configure model processing options to meet constraints
  • select APIs that meet business constraints

Integrate AI models into solutions (25-30%)

Orchestrate an AI workflow

  • define and develop AI pipeline stages
  • 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

  • set up prerequisite components and input datasets to allow consumption of Cognitive Services APIs
  • configure integration with Azure Services
  • set up prerequisite components to allow connectivity with Bot Framework

Integrate Intelligent Edge with solutions

  • connect to IoT data streams
  • design pre-processing and processing strategy for IoT data
  • implement Azure Search in a solution

Deploy and manage solutions (20-25%)

Provision required cloud, on-premises, and hybrid environments

  • create and manage hardware and software environments
  • deploy components and services required to benchmark and monitor AI solutions
  • create and manage container environments

Validate solutions to ensure compliance with data privacy and security requirements

  • manage access keys
  • manage certificates
  • manage encryption keys

Monitor and evaluate the AI environment

  • identify differences between KPIs and reported metrics and determine root causes for differences
  • identify differences between expected and actual workflow throughput
  • maintain the AI solution for continuous improvement

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Related certifications

Microsoft Certified: Azure AI Engineer Associate

Azure AI Engineers use Cognitive Services, Machine Learning, and Knowledge Mining to architect and implement Microsoft AI solutions involving natural language processing, speech, computer vision, bots, and agents.

*Pricing does not reflect any promotional offers or reduced pricing for Microsoft Imagine Academy program members, Microsoft Certified Trainers, and Microsoft Partner Network program members. Pricing is subject to change without notice. Pricing does not include applicable taxes. Please confirm exact pricing with the exam provider before registering to take an exam.

Additional resources

Guides to Role-based Certifications

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Training guide

Discover training resources to become a Microsoft Certified: Azure AI Engineer Associate.