Optimize Data Pipelines DEA-C02 Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A data team uses Snowpipe to load data from an external stage continuously. They notice that some files are not being loaded within the expected time. They want to monitor the Snowpipe performance and identify any errors. Which view or function should they use to get detailed information about the Snowpipe load history?

   • ACOPY_HISTORY
   • BACCOUNT_USAGE.COPY_HISTORY with the pipe name filter
   • CINFORMATION_SCHEMA.PIPE_STATUS
   • DPIPE_USAGE_HISTORY
   Reveal Solution Discussion Select an answer first
2. 2foundation · easy

   Which Snowflake feature provides a built-in view of query performance metrics, including execution time, bytes scanned, and credits used?

   • AQUERY_HISTORY view
   • BWAREHOUSE_METERING_HISTORY view
   • CCOPY_HISTORY view
   • DSTAGE_STORAGE_USAGE view
   Reveal Solution Discussion Select an answer first
4. 3application · medium

   A company runs a Snowflake pipeline that uses a warehouse with a fixed size. During peak hours, queries experience queuing, and the pipeline's SLA is at risk. The team wants to reduce queuing without permanently increasing the warehouse size. Which configuration change would best address this?

   • AEnable multi-cluster with auto-scaling on the warehouse
   • BIncrease the warehouse size to a larger fixed size
   • CUse a smaller warehouse and increase the timeout
   • DSet a resource monitor to limit credit usage
   Reveal Solution Discussion Select an answer first
5. 4foundation · easy

   A data engineer wants to proactively detect when a pipeline's queries are starting to queue due to warehouse concurrency limits. Which metric should they monitor?

   • AAverage query execution time
   • BNumber of queries in the 'Queued' state
   • CBytes scanned per query
   • DCredits used per hour
   Reveal Solution Discussion Select an answer first
6. 5application · medium

   A data pipeline uses a sequence of SQL statements to transform data. The pipeline runs on a Medium warehouse and completes in 30 minutes. Recently, the pipeline started taking 45 minutes. The team checks the query history and sees that one transformation query is now scanning more partitions than before. Which monitoring metric would best confirm the cause of the slowdown?

   • ANumber of errors in the pipeline
   • BBytes scanned by the transformation query
   • CPercentage of queries queued
   • DCredits consumed by the warehouse
   Reveal Solution Discussion Select an answer first