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A company has built a data pipeline using Snowpipe to ingest files from an Amazon S3 bucket. Snowpipe is
configured to load data into staging database tables. Then a task runs to load the data from the staging database
tables into the reporting database tables.
The company is satisfied with the availability of the data in the reporting database tables, but the reporting
tables are not pruning effectively. Currently, a size 4X-Large virtual warehouse is being used to query all of
the tables in the reporting database.
What step can be taken to improve the pruning of the reporting tables?
A user is executing the following command sequentially within a timeframe of 10 minutes from start to finish:
What would be the output of this query?
What built-in Snowflake features make use of the change tracking metadata for a table? (Choose two.)
How can the Snowpipe REST API be used to keep a log of data load history?
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most
recently, poor query performance has been reported.
What could be causing this?
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