Free sections from Ultra Transcenders DP-600: Implementing Analytics Solutions Using Microsoft Fabric, on the comparisons and decisions the exam keeps asking about.
How the two Direct Lake flavours differ in table discovery, permission checks, fallback and unsupported cases, and which one to choose.
When each table storage mode fits a semantic model, based on data size, latency, source security and capacity.
What each Fabric workspace role can do across Power BI, data engineering, warehousing and real-time items.
Which items and settings a deployment copies or leaves alone, and how data source and parameter rules point each stage at its own data.
How data type, team skills, write needs and transactions decide between Fabric's lakehouse, warehouse, eventhouse and other stores.
What the Warehouse can do that a lakehouse's read-only SQL analytics endpoint can't, and when to use each.
The three many-to-many scenarios in a semantic model and the bridge-table or relationship design each one needs.
How data volume, transformation needs, skills and latency decide which Fabric tool should copy data into OneLake.