Analyze solution requirements (25-30%)
Recommend Azure 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 Azure 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 Microsoft 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 of data
design strategies to ensure that the solution meets data privacy regulations and industry
standards
Implement and monitor AI solutions (25-30%)
Implement an AI workflow
develop AI pipelines
manage the flow of data through the solution components
implement data logging processes
define and construct interfaces for custom AI services
create solution endpoints
develop streaming solutions
Integrate AI services and solution components
configure prerequisite components and input datasets to allow the consumption of
Azure Cognitive Services APIs
configure integration with Azure Cognitive Services
configure prerequisite components to allow connectivity to the Microsoft Bot Framework
implement Azure Cognitive 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 an AI solution for continuous improvement
monitor AI components for availability
recommend changes to an AI solution based on performance data
The exam guide below shows the changes that were implemented on May 20, 2020.
Audience Profile
Candidates for this exam should have subject matter expertise using cognitive services, machine
learning, and knowledge mining to architect and implement Microsoft AI solutions involving
natural language processing, speech, computer vision, and conversational AI.
Responsibilities for an Azure AI Engineer include analyzing requirements for AI solutions,
recommending the appropriate tools and technologies, and designing and implementing AI
solutions that meet scalability and performance requirements.
Azure AI Engineers translate the vision from solution architects and work with data scientists,
data engineers, IoT specialists, and software developers to build complete end-to-end solutions.
A candidate for this exam should have knowledge and experience designing and implementing
AI apps and agents that use Microsoft Azure Cognitive Services, Azure Bot Service, Azure
Cognitive Search, and data storage in Azure. In addition, a candidate should be able to
recommend solutions that use open source technologies, understand the components that
make up the Azure AI portfolio and the available data storage options, and understand when a
custom API should be developed to meet specific requirements.
Skills Measured
NOTE: The bullets that appear below each of the skills measured are intended to illustrate how
we are assessing that skill. This list is not definitive or exhaustive.
NOTE: In most cases, exams do NOT cover preview features, and some features will only be
added to an exam when they are GA (General Availability).
Analyze solution requirements (25-30%)
Recommend Azure 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 Azure 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 Microsoft 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 of data
design strategies to ensure that the solution meets data privacy regulations and industry
standards
Implement and monitor AI solutions (25-30%)
Implement an AI workflow
develop AI pipelines
manage the flow of data through the solution components
implement data logging processes
define and construct interfaces for custom AI services
create solution endpoints
develop streaming solutions
Integrate AI services and solution components
configure prerequisite components and input datasets to allow the consumption of
Azure Cognitive Services APIs
configure integration with Azure Cognitive Services
configure prerequisite components to allow connectivity to the Microsoft Bot Framework
implement Azure Cognitive 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 an AI solution for continuous improvement
monitor AI components for availability
recommend changes to an AI solution based on performance data