AI-100 Designing and Implementing an Azure AI Solution (beta)

Languages: English
Audiences: IT Professionals
Technology: Microsoft Azure

Skills measured
This exam measures your ability to accomplish the technical tasks listed below. The percentages indicate the relative weight of each major topic area on the exam. The higher the percentage, the more questions you are likely to see on that content area on the exam. View video tutorials about the variety of question types on Microsoft exams.

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Analyze solution requirements (20-25%)
Identify storage solutions
May include but is not limited to: 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
May include but is not limited to: 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
May include but is not limited to: 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
May include but is not limited to: Identify appropriate services/tools for the solution, identify integration points with other Microsoft services

Design solutions (30-35%)
Design an AI solution that includes one or more pipelines
May include but is not limited to: Define a workflow process, design a strategy for ingesting data
Design the compute infrastructure to support a solution
May include but is not limited to: Define infrastructure types, determine whether to create a GPU-based or CPU-based solution
Design Intelligent Edge solutions
May include but is not limited to: Identify appropriate tools for a solution, design solutions that incorporate AI pipeline components on Edge devices
Design data governance
May include but is not limited to: 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
May include but is not limited to: Choose a cost-effective data topology, configure model processing options to meet constraints, select APIs that meet business constraints

Integrate AI models into solutions (25-30%)
Orchestrate an AI workflow
May include but is not limited to: 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
May include but is not limited to: 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
May include but is not limited to: Connect to IoT data streams, design pre-processing and processing strategy for IoT data, implement Azure Search in a solution

Deploy and manage solutions (20-25%)
Provision required cloud, on-premises, and hybrid environments
May include but is not limited to: 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
May include but is not limited to: Manage access keys, manage certificates, manage encryption keys
Monitor and evaluate the AI environment
May include but is not limited to: 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

Preparation options
Find classroom and online training
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Who should take this exam?
Candidates for this exam analyze requirements for AI cloud-based and hybrid AI solutions, recommends appropriate tools and technologies, and implements solutions that meet scalability and performance requirements.

Candidates are aware of the various components that make up the Microsoft Azure AI portfolio, related open source frameworks and technologies, and available data storage options. Candidates use their understanding of cost models, capacity, and best practices to architect and implement AI solutions.

Candidates should have a working knowledge of basic statistics, data ethics, and data privacy.
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Note
This preparation guide is subject to change at any time without prior notice and at the sole discretion of Microsoft. Microsoft exams might include adaptive testing technology and simulation items. Microsoft does not identify the format in which exams are presented. Please use this preparation guide to prepare for the exam, regardless of its format. To help you prepare for this exam, Microsoft recommends that you have hands-on experience with the product and that you use the specified training resources. These training resources do not necessarily cover all topics listed in the “Skills measured” section.

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