Snowflake SPS-C01 valid study dumps : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A Snowpark developer is using to create a Snowpark session. They want to ensure that the session uses a specific role and warehouse, but only if those parameters are not already defined in the Snowflake CLI configuration. Which of the following code snippets correctly implements this behavior?

A)

B)

C)

D)

E)

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

Which of the following statements about this code are TRUE?

A) The UDF is registered as a permanent UDF and stored in the specified stage for future use.
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 'replace=True' argument ensures that any existing UDF with the same name ('ADD_SALUTATION') is overwritten.
E) The 'input_types' parameter is redundant because Python's type hints are automatically used to determine the input types.

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

4. You have a Snowflake table containing JSON data with nested arrays and objects representing website user interactions. You want to extract all 'product_id' values from within an array named 'viewed _ products' nested inside a 'session' object for each event, using Snowpark for Python. Assume the 'raw_events' table has a variant column called 'event_data". Which of the following Snowpark code snippets will correctly extract and flatten the 'product_id' values into a DataFrame?

A)

B)

C)

D)

E)

5. You've transformed a large Snowpark DataFrame and want to persist it to a Snowflake stage for downstream applications. Your requirements are: 1. The data must be written in CSV format. 2. The files must be GZIP compressed. 3. A header row should be included in each file. 4. The files should be stored in a stage named 'customer_stage' in your Snowflake database. Which of the following code snippets correctly implements this, ensuring optimal performance and resource utilization?

A) Option E
B) Option A
C) Option C
D) Option B
E) Option D

Solutions: