I’m thrilled to have earned the Microsoft Certified: Fabric Data Engineer Associate (DP-700) certification! Thanks @Will Needham for learning materials and this community.

I am thrilled to share that today I earned my Microsoft Fabric Analytics Engineer Associate Certification. Special thanks to @Will Needham for the Fabric Community and content on Microsoft Fabric.

The DP-600 exam is great for giving you an appreciation for a wide range of topics, but does it really prepare you for life as an Analytics Engineer? From speaking with a few of you, it seems you don't feel ready or comfortable yet, which is completely understandable! The DP-600 tests your understanding of the theory, but in the real world, you are measured by your experience and ability to build solutions (how you can apply the theory). As such, I always advise for people to get hands-on as early and as frequently as possible. One of the best Microsoft resources is this list of hands-on exercises which guide you through a number of scenarios, and help you build confidence with the different tools that Fabric provides. Have you tried these resources yet? Let me know what you think! PS: I'm thinking about building a collection of hands-on tutorials which cover a lot of the core analytics engineering/ data engineering (and maybe wider?) tasks that you could expect in your career - would you find that useful?

For below question, which one is correct? Table.Max or Table.Profile? You have a Fabric tenant that contains a warehouse. You use a dataflow to load a new dataset from OneLake to the warehouse. You need to add a PowerQuery step to identify the maximum values for the numeric columns. Which function should you include in the step? A. Table.MaxN B. Table.Max C. Table.Range D. Table.Profile

Fabric CAT Tools was a GitHub repo created and managed by Michael Kovalsky (member of the Fabric CAT). It contains a number of useful methods for automating things in Fabric and Power BI semantic models. Now, it has been renamed and moved to the official Microsoft GitHub repo: https://github.com/microsoft/semantic-link-labs/ It is also published to PyPI (under the name semantic-link-labs)