DP-600 Fabric Analytics Engineer Practice Exam

Preserving historical states of a dimension to enable point-in-time analysis is the idea here. When a dimension attribute changes, a new version of the row is created instead of updating the existing one. This is the approach of a slowly changing dimension of type 2: each change results in a new row with its own surrogate key and a validity period, often represented with start and end timestamps (or a current flag). With this setup you can look back at any date and reconstruct how the dimension looked at that moment by selecting the version whose validity contains that date. This works well for analyses that need to know attributes as they existed in the past, such as a customer’s status or product attributes at a specific time. It’s different from updating the current row (type 1), which overwrites history; it’s different from storing previous values in separate columns for only a subset of attributes (type 3); and it’s different from not tracking changes at all (type 0). That combination of versioned rows and timestamps is why this approach is chosen for point-in-time analysis.