With a massive amount of focus on AI across the Microsoft platform, I decided to sit the new AI-901 exam, which is the new Azure fundamentals exam. I’m far from being an Azure architect, but will freely admit a decent amount of familiarity with a lot of Azure components, especially the AI stuff. Having previously passed the AI-900 a while back, I was expecting the exam to be up to date with technical developments, but wasn’t FULLY prepared for what it was actually like…
Now obviously all Microsoft AI capabilities, regardless of where they’re surfaced through, actually sit (somewhere) within Azure. After all, Azure is the Microsoft cloud platform itself (well, until someone decides to rename it, of course).
My expectations for going into the exam (with admittedly very minimal preparation for it) was to cover the basics for AI within Azure, similar to the way that the AI-900 exam was. Whilst this was somewhat the case, it didn’t necessarily stay within the bounds of my expectations.
The official description of the proposed exam candidate is:
This certification is intended for individuals who want to start working with AI solutions built on Azure. It is suitable for learners from technical backgrounds, including aspiring junior developers who are starting to incorporate AI capabilities into applications. As a candidate for this certification, you should have familiarity with the self-paced or instructor-led learning material.
This certification assesses your ability to show the conceptual knowledge and practical understanding needed to work with AI solutions on Azure, including:
- Understanding core cloud concepts, such as services and resource deployments
- Using Microsoft Foundry to deploy models and implement single-agent solutions
- Recognizing how client applications are put together and how AI models and services are consumed within those solutions
- Understanding Python code examples that call AI models and services
This certification is intended to validate skills commonly used when performing tasks such as:
- Adding AI workloads, including language, vision, and generative AI, to software or IT solutions
- Exploring and using AI features in applications as a junior or entry level developer
The overall information for the exam can be found at Microsoft Certified: Microsoft Azure AI Fundamentals, and there is an official Learning Path available for it.
As I’ve posted before around my exam experiences, 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!). It’s also in beta at the moment, which means that things can obviously change for when it comes out of beta.
My main shock was the number of questions on Python code, including needing to select the right code syntax to use. Whilst I do understand that Microsoft is aiming to make Fundamental level exams/certifications more ‘technical’, I do feel that this is much more technical than the audience should be experiencing. I’ve also fed this back as feedback into Microsoft.
I’ve tried to group things as best together as I feel (in my recollection), to make it easier to revise.
- Analysis
- Analyser types (audio, document, image, video). What each type is, how to configure them, and when to use them
- Defining schemas for data extraction
- How to extract content for analysis
- Python
- Using the Python SDK
- Python code syntax and commands
- Microsoft Foundry/Foundry Models
- How AI models actually work when using/interfacing with them. Behaviour, access to content, prediction etc
- LLM evaluations – comparing costs and capabilities
- Creating, configuring, deploying, updating
- Model temperature, inference
- Minimising model bias, ensuring fairness
- Connecting to a deployed model
- Message structures for Foundry projects
- Agent Evaluators – what they are, how to use them
- Using Azure Content Understanding
- Usage for models
- Using Azure functions
- Encoding images – data types
- Voice Live (audio to text)
- Azure speech SDK, and classes to use
- Prompts:
- Agent prompts. What are they, how are they used, why you should use them
- System prompts. What are they, how are they used, why you should use them
- Microsoft Responsible AI Principles – what they are, what are example of them
- Why humans are still important to be involved in processes
I hope that this is helpful for anyone who’s thinking of taking it – good luck, and please do drop a comment below to let me know how you found it! I’d also be interested in your thoughts/opinions around the direction that Microsoft has taken for this!
