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Snowflake SPS-C01 Exam Overview:

Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

You have a Python UDTF that calculates a running average from a stream of numerical data'. The UDTF's 'process' method maintains state (the running sum and count) between calls. You need to ensure that the UDTF's state is properly initialized for each new group of data processed within a Snowpark DataFrame. What are the requirements?

A. The UDTF class must define a 'end_partition' method to finalize processing and avoid memory leaks.
B. The UDTF class must define a '___init___' method to initialize the state variables and also 'reset method. This '___init____' and 'reset methods will be called once per UDTF instance.
C. The UDTF class must define a 'reset' method. This method will be called by Snowpark at the beginning of processing each group of rows.
D. The UDTF class must have an '__init____' method to initialize the state variables. This '___init____' method will be called once per UDTF instance.
E. The UDTF class must define a '___del___' method. This method will be called by Snowpark at the beginning of processing each group of rows.

Question 2

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?

A.

B.

C.

D.

E.

Question 3

A Snowpark Python application is failing intermittently with a 'net.snowflake.client.jdbc.SnowflakeSQLException: SQL execution error: Remote service internal error [Errorld: ...l' when calling 'df.collect()' on a DataFrame that results from joining multiple tables and applying a complex filter. The data volume is substantial, but within the warehouse's expected capacity. Which of the following actions are MOST likely to resolve this issue? (Select two)

A. Break down the complex query into smaller, intermediate DataFrames and persist them using to avoid memory pressure during a single large query.
B. Increase the parameter to a higher value to prevent session timeouts.
C. Switch to using the function with a raw SQL query instead of Snowpark DataFrame operations.
D. Replace with 'df.toPandas(Y to improve memory management on the client side.
E. Implement retry logic around the 'df.collect()' call with exponential backoff, assuming the error is transient due to resource contention.

Question 4

A Snowpark application is designed to process data residing in a Snowflake table called 'ORDERS'. The application needs to create a temporary view named 'TEMP ORDERS VIEW based on a filtered subset of this table. The view should only be accessible within the current Snowpark session and should be automatically dropped when the session ends. What is the MOST efficient and correct Python code snippet using Snowpark to achieve this?

A.

B.

C.

D.

E.

Question 5

You have two Snowpark DataFrames, 'dfl' and 'df2', representing customer data'. 'dfl' contains customer IDs and names, while 'df2' contains customer IDs and email addresses. You need to create a new DataFrame that contains all customer IDs, names, and email addresses, including customers present in only one of the DataFrames. Which Snowpark set operation and join type would be most appropriate for achieving this?

A. UNION ALL and INNER JOIN
B. UNION and FULL OUTER JOIN
C. INTERSECT and LEFT JOIN
D. DISTINCT and CROSS JOIN
E. EXCEPT and RIGHT JOIN

Solutions: