I'm sitting for the DAC in about nine weeks and trying to figure out where to put the most effort. I've been doing data analytics work for about four years — mostly SQL, some Python, a fair amount of Tableau — but I know exams don't always test the same things you actually use day-to-day.
I've been doing around two hours of studying most evenings, which I can sustain but don't have much room to push beyond. So far I feel okay about the technical analysis sections but I'm less confident in the data governance and ethics content. That stuff isn't something I deal with much in my current role and I don't have a great intuition for how it'll be framed on the exam.
If you've taken this exam recently, I'd love to know which areas surprised you the most. Also curious whether the statistics concepts go very deep — I'm comfortable with regression and descriptive stats but I haven't touched Bayesian methods in a long time and I'm not sure if I need to go back there.
I scored a 74% and the area that dragged me down was visualization best practices. It seems basic, but the exam gets into specific principles around accessibility, cognitive load, and chart selection in ways that go beyond what most of us practice intuitively.
The governance and ethics questions were more nuanced than I expected. They weren't straightforward right-or-wrong scenarios — a lot of them involved situational judgment. I'd give that section a solid two weeks of focused reading.
Coming from a four-year SQL and Python background you're probably in good shape on the technical side. The data quality and pipeline lifecycle questions caught me off guard — less about writing code and more about process knowledge.
Stats didn't go super deep for me, but knowing your probability distributions and being able to interpret p-values confidently is necessary. I didn't see anything requiring Bayesian computation, but conceptual understanding came up a few times.