Ingest Data into Unity Catalog DP-750 Practice Questions (Page 3)

Questions 11–15

1. 11application · medium

   A data engineer needs to ingest a change data capture (CDC) feed from a source database into a Unity Catalog Delta table. The feed contains INSERT, UPDATE, and DELETE operations. The engineer wants to use Spark Structured Streaming in a notebook and maintain the target table as an accurate replica of the source. Which approach should the engineer use?

   • AUse COPY INTO to load the CDC feed files and then run a batch MERGE.
   • BUse foreachBatch to apply the CDC changes to the Delta table using MERGE for inserts and updates, and DELETE for deletes.
   • CUse append mode to write the CDC changes directly to the Delta table.
   • DUse CREATE OR REPLACE TABLE on each micro-batch to replace the table with the CDC changes.
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2. 12foundation · easy

   Which Databricks tool is specifically designed to ingest batch data into Unity Catalog through a no-code or low-code interface, allowing you to configure sources and targets without writing code?

   • AAuto Loader
   • BDatabricks CLI
   • CLakeflow Connect
   • DDatabricks SQL
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4. 13foundation · easy

   Which Databricks service supports continuous streaming ingestion into Unity Catalog with a low-code interface, including monitoring and checkpointing?

   • ALakeflow Connect
   • BDatabricks Repos
   • CDatabricks SQL
   • DDatabricks Workflows
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5. 14foundation · easy

   Which method is used to start a streaming query that writes a streaming DataFrame to a Unity Catalog table?

   • Aspark.streaming.start(df, "table")
   • Bdf.writeStream.outputMode("append").start("catalog.schema.table")
   • Cdf.writeStream.trigger(once=True).saveAsTable("table")
   • Ddf.write.saveAsTable("catalog.schema.table")
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6. 15expert · hard

   A data engineer is setting up a streaming pipeline that reads from Azure Event Hubs and writes to a Unity Catalog table. The pipeline must be resilient to Event Hubs outages and must not lose data. The engineer wants to use a notebook with Spark Structured Streaming. Which configuration is required to ensure no data loss?

   • AEnable checkpointing and use the `eventhubs` source with a consumer group
   • BUse `writeStream.format("delta").outputMode("append")` without checkpointing
   • CUse `foreachBatch` with a MERGE statement
   • DUse the `kafka` source with the Event Hubs connection string
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