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.

Required exam: Exam AI-100

Optional prerequisite: 1 exam | See details

Skills measured

Effective June 25, 2019, this exam will be refocused to increase coverage of Azure technologies for data science, IoT, and AI purposes. The exam has a more aligned focus on Azure cognitive services and bots, with an emphasis on how these services meet business and technical requirements. For more information on the updated content areas, see Exam AI-100.

Analyze solution requirements

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

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

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

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

Optional prerequisite

A helpful starting point for individuals just starting in technology or thinking about a career change.

Microsoft Certified: Azure Fundamentals

Prove that you understand cloud concepts, core Azure Services, Azure pricing and support, and the fundamentals of cloud security, privacy, compliance and trust.

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Exam AI-100

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

Languages: English

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. Learn more.

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