Ingest and Transform Batch Data DP-700 Practice Questions (Page 3)

Questions 11–15

1. 11foundation · easy

   What is the primary purpose of creating a OneLake shortcut?

   • ATo compress data to reduce storage costs
   • BTo create a physical copy of data for redundancy
   • CTo encrypt data at rest
   • DTo provide a virtual reference to data stored in another location without copying it
   Reveal Solution Discussion Select an answer first
2. 12expert · hard

   A data engineer is building a PySpark transformation in a Fabric notebook. The input is a Delta table that contains daily sales data. The table may contain duplicate OrderID values due to late-arriving data. The engineer needs to produce a clean table with one row per OrderID, keeping the row with the latest LastModified timestamp. The engineer also needs to handle the case where two rows have the same OrderID and the same LastModified timestamp. In that case, any row can be kept. What is the best approach?

   • AUse a window function with row_number() partitioned by OrderID ordered by LastModified desc, then filter for row_number = 1.
   • BUse groupBy('OrderID').agg(max('LastModified')) and join back to the original table.
   • CUse dropDuplicates(['OrderID']) and keep the first occurrence.
   • DUse distinct() on the DataFrame.
   Reveal Solution Discussion Select an answer first
4. 13application · medium

   A data engineer needs to transform a large batch of JSON files (several GB) stored in OneLake. The transformation requires joining with a reference table, filtering, and aggregating by customer. The engineer wants to write the result to a Delta table. The team prefers a code-first approach and needs to scale out the processing. Which tool should be used?

   • AT-SQL query in SQL analytics endpoint
   • BNotebook with PySpark
   • CDataflow Gen2
   • DKQL queryset
   Reveal Solution Discussion Select an answer first
5. 14expert · hard

   A data engineering team needs to transform a large batch of JSON files into a star schema. The transformation involves complex business logic, including parsing nested JSON, joining with reference data, and applying multiple conditional rules. The team has strong Python skills but limited SQL experience. The data volume is several terabytes, and the transformation must be scalable. Which tool should you recommend?

   • AUse Dataflow Gen2.
   • BUse KQL queries.
   • CUse T-SQL queries in a warehouse.
   • DUse a PySpark notebook in Fabric.
   Reveal Solution Discussion Select an answer first
6. 15foundation · easy

   Which of the following is a supported source for data mirroring in Microsoft Fabric?

   • AAzure SQL Database
   • BAzure Blob Storage
   • CAzure Files
   • DOn-premises SQL Server
   Reveal Solution Discussion Select an answer first