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.

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Required exams

Image of Exam-AI-100

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


Skills and knowledge

Candidates who earn an Azure AI Engineer certification are verified by Microsoft to have the following skills and knowledge.

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 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
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
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