Power BI basic

A: That’s the most common mistake — confusing time length with sample count. The difference matters because Power BI doesn’t infer physics; it just averages numbers. At 1 Hz you’ve got 900 samples in 15 minutes, so 4,500 ÷ 900 gives 5 kPa. Anything else quietly inflates the KPI and masks drift.

A: That’s the most common mistake — assuming zeros mean process zeros. The difference matters because gateways can pull summaries while PI is still compacting snapshots, returning empty intervals that Power BI pads with zero. DAX and RLS don’t create perfectly aligned 30‑minute voids.

A: That’s the most common mistake — treating dataset size as the only constraint. The difference matters because DirectQuery trades latency for scale, and 3 seconds is acceptable here. Import without aggressive rollups will blow the model limit long before users see value.

A: That’s the most common mistake — letting Power BI invent calendars. The difference matters because mixed‑granularity data needs a single governed date table or you double‑count months. Chart types and implicit measures don’t fix that structural error.