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Microsoft Certified Azure AI Engineer Associate

EXAM CODE AI-100

About

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.

Who should take this exam?

Candidates for this exam analyze the requirements for AI solutions, recommend appropriate tools and technologies, and implements solutions that meet scalability and performance requirements.

Candidates translate the vision from solution architects and work with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end solutions. Candidates design and implement AI apps and agents that use Microsoft Azure Cognitive Services and Azure Bot Service. Candidates can recommend solutions that use open source technologies.

Candidates understand the components that make up the Azure AI portfolio and the available data storage options.

Candidates implement AI solutions that use Cognitive Services, Azure bots, Azure Search, and data storage in Azure. Candidates understand when a custom API should be developed to meet specific requirements.

Benefits of training with us

  • Intuitive and rewarding online training resources

  • 24/7 access to our unique course materials

  • Custom built practical tasks and challenges
  • Official mock examinations to fully prepare you for any final exams
  • Career advice and CV support once training has been completed
  • Full printable course materials, allowing you take your learning offline at your own convenience

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Notice of planned skills update
Exam AI-100 Update Summary
  • 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.
Updated content areas (effective June 25, 2019)
  • Increased coverage of Microsoft Azure Cognitive Services, Azure Bot Service, and Azure AI portfolio

  • Reduced focus on storage solutions, Intelligent Edge solutions

  • Reduced focus on provisioning required cloud, on-premises, and hybrid environments

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 solutions
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 throughout

  • maintain the AI solution for continuous improvement

  • monitor AI components for availability

  • recommend changes to an AI solution based on performance data

  • Official Microsoft AI-100 Exam

  • Online or classroom training

  • Interactive mock exams

  • Live Lab (access to LIVE software)

  • Free Phone Support

  • Online & Email Support

  • World wide Recognised Qualification

  • Unlimited access for 12 months
  • Microsoft Azure Engineer

  • Lead Azure Engineer

  • C# Azure Engineer

  • Azure Data Engineer

  • Azure Stack Engineer

  • MS Azure Cloud Technical Engineer

  • + More

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