Free Snowflake SPS-C01 Exam Dumps Questions & Answers

You are developing a Snowpark application to load data into a Snowflake table named 'SALES DATA. The DataFrame 'sales_df contains new sales records. You need to insert these records into 'SALES DATA. Which of the following Snowpark DataFrame methods will efficiently perform this operation, considering potential data type mismatches between the DataFrame and the target table? Assume no explicit schema definition is necessary.

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

You have a Snowpark Python UDTF named that performs complex data transformations and you want to share it securely with another Snowflake account. Select ALL the necessary steps and considerations to properly share this UDTF using Snowflake Secure Data Sharing.

• A.  Ensure the UDTF doesn't rely on any external packages or dependencies not available in the target account.
• B.  Grant SELECT privilege on the UDTF to the share.
• C.  Grant OWNERSHIP privilege on the UDTF to the share.
• D.  Create a share and grant usage on the database containing the 'process_data' UDTF to the share.
• E.  Create a secure UDTF and add it to share.

A Snowpark application needs to process a large dataset (1 TB) residing in Snowflake, performing complex transformations. Due to network constraints and the complexity of the transformations, the execution takes a considerable amount of time. The application's end-users are complaining about the latency. Which of the following strategies would MOST effectively enhance performance, specifically targeting the reduction of overall execution time considering the synchronous vs. asynchronous execution models and the implications of the 'block' parameter?

• A.  Implement asynchronous actions with 'block=False' to initiate transformations without waiting for completion, allowing other parts of the application to proceed concurrently. Subsequently, use a mechanism (e.g., polling, callbacks) to retrieve the results when available, leveraging parallel execution to enhance overall responsiveness.
• B.  Utilize synchronous actions to trigger transformations and retrieve results; this approach guarantees immediate availability but might not be optimal for long-running, complex operations.
• C.  Minimize data transfer by applying aggressive filtering and aggregation within Snowflake before transferring data to the Snowpark application for further processing, and use synchronous action with a small warehouse size.
• D.  Employ asynchronous actions with 'block=True' to immediately retrieve results, ensuring data consistency but potentially increasing overall latency due to blocking.
• E.  Increase the Snowflake warehouse size to improve compute capacity, regardless of the synchronous or asynchronous execution model.

You are tasked with creating a Snowpark UDTF (User-Defined Table Function) in Python to process a large CSV file stored in a Snowflake stage. Each row in the CSV represents a transaction, and you need to parse each row and extract specific fields based on a complex set of rules. The UDTF should return a table with the extracted fields. Consider the following code snippet:

• A.  The UDTF will run but will not return any data since the code currently lacks a 'session' object properly initialized for Snowpark operations inside the handler. Ensure the handler method has the session parameter and uses it.
• B.  The code will raise an error because the 'read_csvs function is not available within the Snowpark UDTF context. The input needs to be processed differently.
• C.  The UDTF will fail because the 'yield' statement is being called after using 'return' in the processing block. Remove the yield statement as it is incompatible.
• D.  The UDTF will execute correctly and efficiently in Snowpark, correctly processing each row of the CSV and returning the extracted fields as a table.
• E.  The UDTF will run, but it will be slow due to the use of pandas DataFrame operations within the UDTF. Consider optimizing the code to use Snowpark DataFrame operations instead.

Consider the following Snowpark Python code snippet:

• A.  This code will ovemrite the table if it already exists.
• B.  This code will fail because is not a valid method for Snowpark DataFrames.
• C.  The 'result_df DataFrame will be persisted to the 'AGGREGATED SALES table in the default schema of the user running the code.
• D. 
• E.  The code will fail because there is no call to or on the 'result_df dataframe and Snowflake performs lazy evaluation.