DEA-C02 Exam Dumps | You have a Snowflake table 'raw_data' with columns 'id', 'timestamp', and 'payload'. A stream is defined

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Question 103/149

You have a Snowflake table 'raw_data' with columns 'id', 'timestamp', and 'payload'. A stream is defined on this table. A data pipeline reads changes from the stream and applies transformations before loading the data into a target table. However, the pipeline needs to handle cases where updates to the same 'id' occur multiple times within a short period, and only the latest version of the 'payload' should be processed. How can you achieve this idempotent processing of stream data to ensure only the latest payload is applied to the target table, avoiding duplicates and inconsistencies, using Snowflake streams?

A. Use a regular Snowflake task to periodically merge the stream data into the target table, overwriting any existing records with the same Sid'.

B. Create a materialized view on the stream, grouping by 'id' and selecting the maximum 'timestamp' and corresponding 'payload'. Then, consume the materialized view instead of the stream.

C. When processing data from the stream, use a MERGE statement with a staging table. Load all stream changes into the staging table, then merge from the staging table to the target table using 'timestamp' to identify the latest version.

D. Before loading data into target table, create a temporary table by grouping Sid' and selecting the maximum 'timestamp' and corresponding 'payload' from stream. Finally, load this data into target table.

E. Configure the stream with a unique key constraint on the Sid' column to prevent multiple updates for the same Sid' from being captured.

Question 103/149

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