Snowflake SPS-C01 Exam Questions : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

Consider the following Snowpark code snippet that defines and registers a UDF:

Which of the following statements about this code are TRUE?

A. The 'replace=True' argument ensures that any existing UDF with the same name ('ADD_SALUTATION') is overwritten.
B. The UDF is registered as a temporary UDF and will be removed when the session ends.
C. The default value of 'salutation' in the Python function will be used even when calling the UDF from SQL if the salutation parameter is omitted.
D. The UDF is registered as a permanent UDF and stored in the specified stage for future use.
E. The 'input_types' parameter is redundant because Python's type hints are automatically used to determine the input types.

Question 2

You are developing a Snowpark Python application to process streaming data from a Kafka topic, enrich it with data from a Snowflake table, and store the results in another Snowflake table. The enrichment process involves joining the streaming data with a large dimension table in Snowflake. Which of the following Snowpark features would be most efficient and scalable for this use case, considering the continuous nature of the streaming data and the size of the dimension table?

A. Using a standard Snowpark DataFrame join operation with a broadcast hint on the smaller streaming data DataFrame.
B. Employing a Snowpark Stored Procedure that periodically refreshes a smaller, aggregated version of the dimension table and using that for joining with the streaming data.
C. Creating a Snowflake materialized view that pre-joins the streaming data (landing in a table) with the dimension table and using Snowpark to query the materialized view.
D. Leveraging a dynamic table to incrementally update the join of the streaming data and dimension table.
E. Utilizing Snowpark UDFs to fetch data from the dimension table for each row of the streaming data DataFrame.

Question 3

You have a Snowpark DataFrame with columns 'sale_date', 'product_id', and 'revenue'. You need to calculate the cumulative revenue for each product over time. Which of the following approaches will accomplish this in Snowpark using window functions?

A.

B.

C.

D.

E.

Question 4

Consider a scenario where you have a table 'EMPLOYEES' with columns 'employee id', 'department', and 'salary'. You want to delete employees who belong to either the 'HR' or 'Finance' department and have a salary less than 60000. Which of the following Snowpark DataFrame operations correctly implements this deletion?

A. Option D
B. Option B
C. Option C
D. Option A
E. Option E

Question 5

Consider the following Snowpark Python code snippet for creating a stored procedure:

What is the PRIMARY reason for explicitly defining 'input_types' and during the stored procedure registration?

A. To allow Snowsight to correctly display the stored procedure's metadata, making it easier for users to understand its functionality.
B. To allow Snowflake to automatically generate documentation for the stored procedure's input and output types.
C. To ensure data type safety and schema validation during deployment and execution, preventing unexpected runtime errors due to type mismatches between the stored procedure and the calling environment.
D. To enable the stored procedure to be called from other programming languages besides Python.
E. To improve the performance of the stored procedure by enabling compile-time optimizations.

Solutions: