Design Data Ingestion and Transformation Pipelines ARA-C01 Practice Questions (Page 5)

Questions 21–24

1. 21expert · hard

   An e-commerce company maintains a large customer table (500 million rows) that is updated throughout the day. A nightly process needs to synchronize this table to a reporting database. The team wants to minimize the amount of data transferred and avoid reprocessing unchanged rows. The source table has no timestamp column, but the application does issue DELETE and UPDATE statements. Which approach should the architect recommend?

   • AUse a task to compare the source and reporting tables using a hash of all columns to identify changes.
   • BCreate a stream on the customer table and a task that runs every 5 minutes to merge the stream's changes into the reporting table.
   • CAdd a timestamp column to the source table and use a WHERE clause to filter rows modified in the last 24 hours.
   • DUse a full refresh nightly: truncate the reporting table and reload all 500 million rows from the source.
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2. 22foundation · easy

   Which Snowflake feature automatically loads new files as they arrive in an external stage, without requiring manual intervention?

   • AConnector
   • BSnowpipe
   • CExternal table
   • DCOPY INTO
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4. 23foundation · easy

   A data engineering team needs to load data that arrives continuously throughout the day into Snowflake, with a requirement that the data be available for querying within a few minutes of arrival. Which pipeline pattern best fits this requirement?

   • ABatch loading once per day
   • BMicro-batch loading every few minutes
   • CStreaming with per-event inserts
   • DManual ad-hoc loads
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5. 24foundation · easy

   In a streaming ingestion pipeline using Snowpipe, what is a common approach to handle errors in the data being loaded?

   • AUse the COPY INTO error handling options
   • BManually review each file before loading
   • CStore all errors in a separate table automatically
   • DIgnore all errors and continue loading
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