Microsoft Fabric Data Engineer Associate DP-700 Practice Question
You are building a Lakehouse in Microsoft Fabric. A pipeline must ingest 200 GB of historical orders from Azure SQL Database once and then load only rows that changed since the last run each night. The pipeline triggers a Spark notebook that writes data to a Delta table in the Bronze layer. Which approach should you implement in the notebook to meet these requirements?
Use a Delta Lake MERGE INTO operation keyed on a persisted watermark column to upsert nightly changes.
Issue a TRUNCATE TABLE statement on the Delta table followed by a full INSERT of all source rows each night.
Enable Auto Optimize and Auto Compaction on the Delta table without additional logic; Spark will automatically detect and merge changes.
Configure the pipeline to always overwrite the Delta table using COPY INTO with FILEFORMAT = PARQUET and a forced path option.
A Delta Lake MERGE INTO statement lets the notebook upsert only the rows that are new or have changed since the previous load. By filtering the source data with a high-watermark value stored in a control table and then executing MERGE INTO on the target Delta table, the operation inserts new rows and updates existing ones in place, avoiding a full reload and minimizing write operations. Truncating and re-inserting, overwriting with COPY INTO, or relying solely on Auto Optimize/Compaction would reload all data or fail to detect row-level changes, defeating the purpose of an incremental pattern.
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What is a Delta Lake MERGE INTO operation?
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What is a high-watermark column and how is it used in incremental data processing?
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How does Auto Optimize and Auto Compaction work in Delta Lake, and why is it insufficient for incremental loading?
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Microsoft Fabric Data Engineer Associate DP-700
Ingest and transform data
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