Free sections from Ultra Transcenders DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric, on the comparisons and decisions the exam keeps asking about.
How purpose, skills and coding level decide between Dataflow Gen2, a pipeline and a notebook, and where Copy job and Apache Airflow jobs fit.
How authoring style, output, storage, state and latency decide between eventstreams, Spark structured streaming and eventhouses.
When a KQL database should ingest data, query it through a standard OneLake shortcut, or accelerate the shortcut, and what each costs.
How the five eventstream window types group events in time, how they overlap and how to write them in the SQL operator.
When to reload everything or only changes, and which change-detection method catches inserts, updates and deletes.
How starter, custom, capacity and custom live pools differ in node sizes, start-up time, sizing against the capacity and job admission.
The default, email, random and partial masks, the permissions that add or bypass them, and why masking alone doesn't stop inference.
How skills, data location and transformation type decide between Dataflow Gen2, Spark notebooks, KQL update policies and warehouse T-SQL.