DEA-C02 Exam Dumps | You are tasked with processing streaming data in Snowflake using Snowpark Python. The raw data arrives

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Question 39/149

You are tasked with processing streaming data in Snowflake using Snowpark Python. The raw data arrives in a DataFrame raw events' with the following schema: 'event id: string', 'event_time: timestamp', 'user id: string', and 'event data: string'. You need to perform the following data transformations: 1 . Extract a specific value from the JSON 'event_data' using the 'get' function to find the 'product_id' and create a new column named 'product id' of type STRING. 2. Filter the DataFrame to include only events where the is NOT NULL and the is within the last hour. 3. Aggregate the filtered data to count the number of events per 'product id'. Which of the following code snippets correctly performs these transformations in an efficient and performant manner?

A. Option A

B. Option B

C. Option C

D. Option D

E. Option E

Correct Answer: B

Option B correctly addresses all the requirements. It uses 'product_id').cast('string')' to extract the product_id' from the JSON and cast it to a string; filters for non-null 'product_id' values and 'event_time' within the last hour using 'dateadd' and 'current_timestamp' for efficient time filtering; and then groups by 'product_id' to count the events. Option A includes 'to_timestamp' which is unneccessary. Option C extracts the 'product_id' from the JSON string to create 'product_id' without casting it to string type. This could create issues in later aggregations/filtering if the data types do not match up. Option D uses subtraction on 'current_timestamp' with the addition result of 'dateadd('hour', 1, current_timestamp()Y to perform time filtering. This will subtract a future time from the current time. This is incorrect.Option E passes a SQL string directly to the filter, bypassing Snowpark+s function calls.

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