Posted by: kurtsh | September 27, 2020

TRAINING: Tips for “AI-900 Azure AI Fundamentals”

imageI recently passed the AI-900 “Microsoft AI Fundamentals” certification and got a lot of people asking me about it.

I’ve typed up the following information for anyone looking to get the AI-900 cert under their belt because I think they might be gotchas for some. These are the things I personally got tested on.
(Please note that the exam content is changing Oct 20th so take the exam soon if you plan to use the internal materials to prep)

  • Machine Learning
    There is an expectation that you have directly used – or at least witnessed the proper usage of – “Azure Machine Learning Designer” & “Automated ML”.
        • Watch how to build a pipeline, how to prep your dataset, where to apply algorithms, and when to split data.
          Ex: “Where in the pipeline do you split data?” “Why do you do that?” “Where do you apply ML algorithms?”
        • Pay attention to the selection of Automated ML “task types” & what algorithms you can block like ElasticNet, ExtremeRandomTrees, XGBootRegressor, etc.
          Ex: “What are examples of 2 algorithms could be blocked in your Automated ML model training?”
        • Know the difference between “Feature Selection” vs “Feature Engineering” in Automated ML
          Ex: “What are examples of Feature Selection?” “Examples of Feature Engineering?” “When do you use them in the configuring your Automated ML run?”
  • Cognitive Services
    There is an expectation that you can determine when to apply Azure Machine Learning, Azure Cognitive Services, Azure Computer Vision, & Azure Custom Vision – and what is required to implement them in Azure & how to use them in applications.
      • Pay attention to the diversity of services Cognitive Services provides, Computer Vision provides and what Custom Vision offers beyond custom data modeling.
        Ex: “Does Computer Vision offer optical character recognition?” “Do you need to provide custom data for Custom Vision?“Why not use Azure Cognitive Services over Computer Vision?
      • Pay attention to the differences between Text Analytics, Language Understanding Intelligent Service, Bot Service, QnA Maker.
        Ex: “Do you need to provide LUIS with custom models?” “Can you use LUIS with Bot Service?” “Does QnA Maker refresh it’s model when you change your QnA data?”
      • Understand what the I/O parameters of a creating & using a Cognitive Services resource.
        Ex: “What is required to stand up a Cognitive Services resource in Azure?” “Name two properties that a Cognitive Resource produces for you to use it in your application?”


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