Apply Change Data Feed (CDF) to Address Specific Limitations of Streaming Tables and Enhance Latency. DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   What is a key trade-off between using Change Data Feed (CDF) and using a streaming table for incremental processing?

   • AStreaming tables have lower latency but cannot handle updates.
   • BCDF provides lower latency but requires more complex pipeline logic.
   • CCDF adds storage overhead but allows for more flexible change tracking.
   • DCDF is cheaper than streaming tables because it does not require compute.
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2. 7foundation · easy

   Which option is used in a streaming read to specify a timestamp from which to start reading Change Data Feed (CDF) changes?

   • AchangeDataTimestamp
   • BstartingTimestamp
   • CstartingVersion
   • DtimestampAsOf
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4. 8foundation · easy

   How does enabling Change Data Feed (CDF) on a Delta table help reduce latency in a downstream pipeline?

   • AIt automatically compacts the table to reduce the number of files that need to be read.
   • BIt creates a secondary index on the table to speed up lookups.
   • CIt allows the pipeline to process only the changed rows instead of reprocessing the entire table.
   • DIt caches the entire table in memory so that queries run faster.
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5. 9application · medium

   A data engineer is evaluating whether to use a streaming table or a streaming query with Change Data Feed for a new pipeline. The source table is updated with inserts, updates, and deletes. The downstream application needs to see all changes. What is a limitation of using a streaming table in this scenario?

   • AStreaming tables cannot read from Delta tables.
   • BStreaming tables require Change Data Feed to be enabled on the source table.
   • CStreaming tables have a maximum latency of 5 minutes.
   • DStreaming tables only support append-only data and will ignore updates and deletes.
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6. 10application · medium

   A data engineer needs to enable Change Data Feed on an existing Delta table that is used by multiple downstream jobs. The table has a large history of changes. What is the recommended way to enable CDF without disrupting the existing jobs?

   • ARun ALTER TABLE ... SET TBLPROPERTIES (delta.enableChangeDataFeed = true)
   • BSet the Spark configuration 'spark.databricks.delta.changeDataFeed.enabled' to true.
   • CUse the OPTIMIZE command with the 'changeDataFeed' option.
   • DRecreate the table with the CDF property and copy all data.
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