Badge

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 has been 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 (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

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.

Prepare for certification

Self-paced

Free
Microlearning
Interactive
In-browser access
Start learning

Instructor-led

Paid
Personalized
In-person
On-demand
Explore courses

Guide to training

All self-paced and instructor-led courses in one comprehensive guide.

Download

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.

Schedule exam

*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

Training guide

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

Guides to Training and Certifications

Explore all certifications in a concise training and certifications guide or the Training and Certifications poster.

Exam Replay

See two great offers to help boost your odds of success.