This certification demonstrates that the recipient is proficient in analyzing requirements for AI
solutions, recommending the right tools and technologies and designing and carrying out scalable AI
solutions that meet an organization’s performance requirements.
Why Take The Microsoft Certified: Azure AI Engineer Associate AI-100 Exam?
The need for AI tech professionals is going to increase dramatically in the near future and passing
the exam will help you secure an excellent position in the industry as an Azure AI Engineer.
Increase My Salary
- The average salary for someone who holds a Microsoft Certified: Azure AI Engineer
Associate certification is around $134,000 / year
Be Part Of The Team
- As an Azure AI Engineer, you become part of the team that’s dedicated to managing cloud-
based or hybrid environments for your organization’s cloud infrastructure. - Translate the ideas from the solutions architects and work with data scientist, other
engineers, IoT specialists and software developers to create complete solutions.
Abilities Validated By The Certification:
- Analyze solution requirements
- Design AI solutions
- Implement and monitor AI solutions
Recommended Knowledge & Experience:
- Candidates should be knowledgeable and have first-hand 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. - Candidates should be able to recommend open sourced technological solutions, understand
the different parts that make up the Azure AI portfolio and the amount of storage options,
along with knowing when a custom API should be developed to meet the requirements of the
project.
Exam Topics & Scoring:
AI-100 Exam: Designing and Implementing an Azure AI Solution
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
Prepare for your exam:
The best way to prepare is with first-hand experience. Taking advantage of the opportunities that
Phoenix TS provides will assist you with gathering all the knowledge and skills you’ll need for
certification.
Phoenix TS Microsoft Certified: Azure AI Engineer Associate – Learning Pathways
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AI-100T01: Designing and Implementing an Azure AI Solution
Course Overview Phoenix TS’ 3-day instructor-led Microsoft Designing and Implementing an Azure AI Solution training and certification boot camp in Washington, DC Metro, Tysons Corner, VA, Columbia, MD or Live Online teaches you the necessary knowledge for designing Azure AI solution by building a customer support chat Bot using artificial intelligence from the Microsoft Azure […]
