Building and Testing an ETL Pipeline with Lakeflow Spark Declarative Pipelines, SQL, and Apache Spark on the Databricks Platform DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 6)

Questions 26–29

1. 26foundation · easy

   What is the purpose of the 'DataFrame.transform' method in the context of testing?

   • ATo optimize the execution plan of a DataFrame
   • BTo assert that a DataFrame meets certain conditions
   • CTo convert a DataFrame to a different format
   • DTo apply a function to a DataFrame and return a new DataFrame
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2. 27foundation · easy

   Which of the following operations does AUTO CDC automatically handle when applying changes to a target table?

   • AOnly insert and update operations
   • BOnly insert operations
   • CInsert, update, and delete operations
   • DOnly delete operations
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4. 28application · medium

   A data engineer is building a Lakeflow Spark Declarative Pipeline that processes data for multiple regions. The pipeline needs to run the same transformation logic for each region, but the source paths and target tables are different. The engineer wants to avoid duplicating the transformation code. Which approach should they use?

   • AUse a materialized view that filters the data by region.
   • BUse a single notebook that takes the region as a parameter and run it multiple times.
   • CCreate a separate pipeline for each region.
   • DUse a for/each control flow operator to iterate over the list of regions and execute the transformation for each.
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5. 29foundation · easy

   What is the primary purpose of Databricks Jobs in the context of ETL automation?

   • ATo provide a development environment for writing Spark code
   • BTo store and manage large datasets in the cloud
   • CTo visualize data and create dashboards
   • DTo schedule and orchestrate the execution of notebooks, scripts, and pipelines
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