You maintain a Direct Lake semantic model built on a Fabric lakehouse. A large fact table is loaded daily via Spark notebooks that append new partitions to the underlying Delta table. Business users report that the model does not immediately reflect the newly appended data after each daily load, and you want the model to expose the latest data with minimal query latency. Which approach correctly ensures the Direct Lake model reflects the newly loaded data?