Crucial Exams

Microsoft Fabric Data Engineer Associate DP-700 Practice Question

You ingest a daily parquet file of customer data into a Delta table named customer_raw in a Microsoft Fabric lakehouse. You must populate a DimCustomer table that tracks history using a Type 2 slowly changing dimension (SCD) while avoiding full-table reloads. Which transformation pattern should you use to meet the requirements?

  • Execute a PySpark notebook that performs a Delta MERGE between customer_raw and DimCustomer to insert new customers and version changed records with new surrogate keys.

  • Configure a Dataflow Gen2 incremental refresh that truncates DimCustomer and appends the current day's customer records.

  • Run a T-SQL CREATE TABLE AS SELECT (CTAS) statement each day to overwrite DimCustomer with the latest snapshot from customer_raw.

  • Create a KQL materialized view over customer_raw and expose it directly as the DimCustomer dimension.

Microsoft Fabric Data Engineer Associate DP-700
Ingest and transform data
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