A semantic model is slow to refresh a growing table. What helps?
Why D is the answer
Incremental refresh
Incremental refresh processes only recent data.
In detail
- Incremental refresh loads only recent data.
- History stays in the model and is not reloaded.
Use case
An illustrative scenario, not a real incident.
A model reloads ten years of sales each night. Incremental refresh keeps the older partitions and refreshes recent ones.
See it in different ways
sequenceDiagram participant P as Pipeline participant S as Source participant T as Delta table P->>P: Reads the last watermark P->>S: Rows newer than the watermark S-->>P: Changed rows P->>T: MERGE P->>P: Saves the new watermark
mindmap
root(("Sales table"))
n1["Older data"]
n2["Kept as it is"]
n3["Recent data"]
n4["Refreshed"]Key point: Incremental refresh processes recent data.
Why the other options are wrong
- A. A larger report page size
- Page size affects reports, not refresh.
- B. More sensitivity labels
- Labels have no effect on refresh.
- C. Row-level security on the table
- Row filters do not shorten refresh.