AI-900: Microsoft Azure AI Fundamentals

One of my recent decisions has been to explore the Azure space. There are several reasons behind this. CDS, as we (hopefully!) know sits on top of Azure, and it’s useful to know the broader digital estate available on the platform.

I’ve also been looking into some of the Cognitive Services functions that are available within Power Platform. These all live in Azure, and are surfaced into Power Apps etc. It’s therefore good to know what can be done outside of the ‘Power Platform bubble’, and the options there.

Incidentally, a year ago I even built a canvas app that allowed you to take a picture of a motorbike tyre. Using AI Builder functionality, it then analysed if the tyre tread was legal or not! That was a really cool proof of concept.

So a good place to start, I thought, would be with the AI-900. This covers the fundamentals of the AI offerings that are in Azure. I had forgotten though that with fundamental exams, there’s only 60 minutes available! Seeing the timer ticking down from that give me a little surprise, though I managed to get through it (& pass!) in good time.

The official description of the exam is

Candidates for this exam should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.

This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.

This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.

The official page for the exam is at https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900, where it gives quite a good overview of things. Go take a look at it, and also take a look at the associated learning paths.

Once again, I sat the exam through the proctored option (ie from home). Honestly I think that my experience this time has probably been the best so far. I went through the usual system checks for signing in. The proctor came alone, and within 30 seconds they had released the exam!

So, as before, it’s not permitted to share any of the exam questions. This is in the rules/acceptance for taking the exam. I’ve therefore put an overview of the sorts of questions that came up during my exam. (Note: exams are composed from question banks, so there could be many things that weren’t included in my exam, but could be included for someone else!). I’ve tried to group things together as best as possible for the different subject areas.

  • Image recognition types
    • What each one is, what it’s used for
    • When to use for a specific scenario
  • Facial recognition
    • Different types available
    • What each one is, what it’s used for, when to use for a specific scenario
    • Limitations & issues that can occur when using it
  • Text:
    • Different recognition types
    • What each one is, what it’s used for, when to use for a specific scenario
    • Analytics. How this works, how to set up & use
    • Translation. Different options available, how they work, when to use for a specific scenario
    • Sentiment analysis. How it works, limitations, what’s needed to train a model
  • QnA Maker
    • What this does, how to set it up, how to train it
    • Generating material with it
    • Use with chatbots
  • Machine Learning
    • What this actually is, and what it does
    • How it works
    • Different types that are available, how they work, how to train a model
    • Classification options
  • Machine Learning Designer
    • How to use & set up
    • Different types of data/options used within it
    • Training & evaluation models. The steps needed for this, how to set it up correctly
    • Types of modules available
    • Validation sets
  • Chatbots
    • What they are
    • How/where they can be used
    • Limitations
    • Integration with other systems
  • Charts
    • Different charts that are available for use
    • Reading them correctly
    • Model types shown on them
    • Metrics!
  • Microsoft AI Principles
    • The different principles that are published
    • What each one means/refers to

Overall, it was quite good. The Microsoft AI Principles were new to me, and I had to guess at those (I went to look them up afterwards!). Other than that, some bits I breezed through, other parts I took careful stock of.

This is definitely an area that I’m going to continue exploring, and will be writing up further exams that I take in it. I’m curious what your experience of it has been – please drop a comment below to let me know!

The post AI-900: Microsoft Azure AI Fundamentals appeared first on The CRM Ninja.

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