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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q122-Q127):

NEW QUESTION # 122
Given a Snowpark DataFrame 'df with a column named 'data' of VARIANT type, where the VARIANT contains JSON objects with nested fields. You need to extract the value of the nested field 'address.city' as a STRING and the value of as a DOUBLE, handling cases where either 'address' or 'items' might be missing. Which combination of Snowpark functions is best suited to achieve this robustly and efficiently?

Answer: A

Explanation:
Option C is the most robust because it uses 'get_patW combined with and 'get_patm is specifically designed for extracting nested fields from VARIANT data, and handle cases where the path doesn't exist (returning NULL instead of an error).option A will fail if 'address' or 'items' is missing. Option B doesn't handle the NULL cases gracefully and 'to_double' expects a string not a variant. Option D doesn't handle the NULL cases gracefully and used 'to_varchaff which is deprecated. Option E will fail if 'address' or 'items' is missing and doesn't use get_path


NEW QUESTION # 123
You are using Snowpark Python to create a DataFrame from an existing Snowflake table "SALES DATA'. You want to apply a user- defined function (UDF) to each row of the DataFrame to calculate a custom sales metric. The UDF requires access to the 'session' object. Which of the following approaches is correct for defining and applying the UDF in Snowpark?

Answer: A

Explanation:
Option E is the correct approach. To access the session object from within the UDF, you can import it when registering the UDF with the session. It should be imported inside the function and use the decorator. The input_types parameter should be set to 0, since this allows for session access.


NEW QUESTION # 124
A data engineering team wants to deploy a Snowpark Python stored procedure that aggregates sales data from a table 'SALES DATA and writes the results to a table 'AGGREGATED SALES. The stored procedure needs to be executed by various users with different roles. The team wants to ensure that users can only execute the stored procedure and cannot directly access the underlying 'SALES DATA' table. Which approach is most suitable for managing data access and security in this scenario, and what are the implications of using 'EXECUTE AS OWNER vs 'EXECUTE AS CALLER?

Answer: D

Explanation:
'EXECUTE AS OWNER allows the stored procedure to run with the privileges of the stored procedure's owner, effectively hiding the underlying tables from the caller. This is crucial for security. 'EXECUTE AS CALLER requires the caller to have the necessary privileges on the underlying tables, which defeats the purpose of restricting direct access. While creating a view is an option, it adds complexity and may not be necessary if 'EXECUTE AS OWNER is used correctly. Option D mentions 'EXECUTE TASK which isn't the right privilege needed for stored procedures execution. 'EXECUTE AS CALLER in Option E opens up direct access to underlying tables.


NEW QUESTION # 125
A data scientist has developed a Snowpark Python stored procedure named 'model_training'. This procedure utilizes a large machine learning model and requires significant compute resources. The data scientist wants to optimize the cost and performance of running this stored procedure. Which of the following strategies would be the MOST effective for achieving this goal?

Answer: C

Explanation:
Specifying a warehouse size and using auto-suspend and auto-resume provides a balance between performance and cost. Option A might increase costs due to idle time. Option B relies on Snowflake's default warehouse, which might not be optimal. Option D could increase overall execution time due to overhead. Option E may not be feasible or efficient if the logic is heavily dependent on Python libraries. Therefore, specifically assigning an appropriate warehouse size with auto-suspend/resume is the most effective approach.


NEW QUESTION # 126
You have a Snowpark DataFrame containing customer order data with columns , and 'order_amount' . You need to identify customers who placed orders exceeding $1000 on more than 3 separate days. Which Snowpark code snippet correctly achieves this? Assume SparkSession 'spark' and DataFrame are already defined.

Answer: D

Explanation:
Option A correctly filters for orders exceeding $1000, groups by customer ID, counts the distinct order dates, aliases the count as 'distinct_order_days', filters for customers with more than 3 distinct order days meeting the criteria, and then displays the result. Option B counts total number of orders instead of distinct dates. Option C counts all orders exceeding $1000. Option D uses distinct which will remove some of the dates and produce innacurate number of days. Option E is an incorrect syntax.


NEW QUESTION # 127
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