By: Ron L'Esteve | Comments | Related: More > Professional Development Certifications
Problem
Artificial intelligence is a highly lucrative, competitive, and niche field with above-average job growth. A career in AI requires both advanced technical training and hands-on experience. While there are many certifications, trainings, and courses available to help with preparing for a career in AI, the Microsoft Certified: AI Engineer Associate is a certification which covers AI specializations in the Microsoft Azure Platform.
With AI being at the forefront of many modern technology stacks including the Azure Data & AI Platform, there is immense value in investing the time and effort in pursuing the Microsoft Certified: AI Engineer Associate credential by taking the AI-100 Exam. While Microsoft provides a downloadable Exam Skills Outline, how else can we learn more about available resources, links, and study materials that could be used to prepare for this AI-100 exam?
Solution
There are a few online free and paid resources available to assist with preparing for the AI-100 exam. In this article, I will attempt to consolidate some of these useful resources and provide useful tips to help with preparing for the AI-100 exam.
There is quite a lot of material that is covered in the AI-100 exam. Candidates are expected to understand how to analyze, recommend, design, implement, and monitor a variety of AI solutions based on customer needs and requirements.
The journey to becoming a Microsoft Certified: Azure AI Engineer Associate includes preparation, hands-on training, experience, and ultimately passing the required exam to earn your certification.
The sample architecture diagram below illustrates some of the considerations to account for while designing and implementing an Azure AI Solution.
AI-100 Skills Measured
There are numerous skills that will be measured as part of the AI-100 Exam. The skills outline below includes links to relevant Microsoft related articles that will help with exam preparation.
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 Machine Learning solution
- Identify appropriate services and tools for a Cognitive Services solution
- Identify integration points with Event Grid
- Identify integration points with Event Hubs
- Identify integration points with Logic Apps
- 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 pipelines that use AI apps
- Design pipelines that call Azure Machine Learning models
- Select an AI solution that meet cost constraints
Design Solutions that use Cognitive Services
- Design solutions that use computer vision APIs
- Design solutions that use speech APIs
- Design solutions that use language APIs
- Design solutions that use anomaly detection APIs
- Design solutions that use search 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 you can get compliance data & requirements defined by your organization
- Understand what Compliance Manager is along with its components
- Ensure appropriate governance for data
- Design strategies to ensure that the solution meets data privacy and compliance standards
- Design strategies to ensure that the solution meets data retention and privacy standards
Implement and Monitor AI Solutions (25-30%)
Implement an AI Workflow
- Develop AI pipelines
- Manage the flow of data through IoT solution components
- Manage the flow of data through IoT Edge solution components
- Manage the flow of data through Advance Analytics on Big Data solution components
- Manage the flow of data through Advance Analytics solution components
- Implement data logging processes
- Define and construct interfaces for custom AI services
- Create solution endpoints
- Develop streaming solutions
Integrate AI Services with Solution Components
- Configure prerequisite components and input datasets to allow the consumption of Cognitive Services APIs
- Configure integration with Cognitive Services
- Configure prerequisite components to allow connectivity to the 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
- Create custom KPI dashboards using Azure Application Insights
- Identify the differences between expected and actual workflow throughput through scalability and performance
- Monitor and collect data from ML web service endpoints
- Maintain an AI solution for continuous improvement
- Monitor AI components for availability through Azure Resource Logs
- Monitor AI components for availability through Application Insights telemetry data model
- Recommend changes to an AI solution based on performance data
AI-100 Exam Preparation Resources
In addition to preparing for the AI-100 Exam by studying the Microsoft articles outlined in the previous skills section, there are also numerous free and paid resources available to assist and supplement the exam preparation process. In this section, I have included a few of these available resources that will help with preparing for AI-100.
- Microsoft's Learn Platform: There are around 58 free learning modules for the AI Engineer track including the basics of Azure, AI, and as numerous hours of free hands-on technical modules to help prepare for AI-100.
- Microsoft's Hands-On with AI: To get more hands on with AI, check out Microsoft's Hand-On with AI website and dive in to interactive demos that showcase a selection of the capabilities of the Microsoft AI platform.
- Pluralsight Courses: With a free 7-day trial, Pluralsight offers 14 courses to help prepare for AI-100 that can be completed in around 26 hours.
- Udemy Exam Prep Resources: There are a number of reasonably priced courses and exam prep material available at Udemy to help prepare for the AI-100 exam.
- GitHub Labs: This is one of many available GitHub repos that contain detailed hands-on labs to help prepare for AI-100.
Glossary of Useful Terms
Finally, here is a list of useful terms (along with clickable links) that may appear on the AI-100 exam in some shape or form. Be sure to have a basic understanding of these terms and how they fit into the design and implementation of an Azure AI Solution.
Next Steps
- After you have prepared for this AI-100 Exam, head over to Microsoft's AI-100 Certification and Exam page to schedule and take the exam.
- Explore Azure Machine Learning and consider pursing the Azure Data Scientist certification.
- Read my article on Realizing Business Value through an AI-driven Strategy and Culture.
- Think big picture and explore Microsoft's AI Business School.
About the author
Ron L'Esteve is a trusted information technology thought leader and professional Author residing in Illinois. He brings over 20 years of IT experience and is well-known for his impactful books and article publications on Data & AI Architecture, Engineering, and Cloud Leadership. Ron completed his Master’s in Business Administration and Finance from Loyola University in Chicago. Ron brings deep tecThis author pledges the content of this article is based on professional experience and not AI generated.
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