Microsoft Certified Azure AI Engineer Associate

Azure AI Engineer Associate

Knowledge Area: Analyse 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

Knowledge Area: 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

Knowledge Area: 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

Knowledge Area: 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

Zie Exam Track...

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