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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

You have a Snowpark application that processes sensor data from a large number of devices. You've implemented a scalar UDF in Python to calculate a complex statistical metric for each sensor reading. Initial tests show poor performance. Which of the following strategies would be MOST effective in improving the performance of this application, considering the nature of the computation and the data volume?

A. Increase the warehouse size significantly to provide more resources for the scalar UDF execution.
B. Rewrite the UDF logic directly in SQL, leveraging Snowflake's built-in functions for statistical calculations.
C. Implement caching for the UDF results, assuming the statistical metric calculations are frequently repeated for the same sensor readings.
D. Replace the Python scalar UDF with a Java scalar UDF, as Java typically offers better performance than Python in Snowflake.
E. Replace the Python scalar UDF with a Python vectorized UDF, ensuring the UDF processes data in batches instead of row-by-row.

Question 2

Consider a Snowpark DataFrame with columns 'DEPARTMENT, 'SALARY , and 'YEAR. You want to find the average salary for each department over all years and then filter the departments to only include those where the average salary is greater than 100000. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowpark Python?

A.

B.

C.

D.

E.

Question 3

You have a Snowpark Python application that performs several data transformations. You need to implement error handling to catch exceptions during DataFrame operations and log them to a Snowflake table named 'error _ log'. You also need to ensure that if any exception occurs, the application continues to run and processes the remaining data. Which of the following approaches (or combination of approaches) would be MOST suitable?

A. Implement a custom User-Defined Function (UDF) that encapsulates the error-prone logic. Within the UDF, use try-except blocks to handle errors and return error messages along with the processed data. Add a column in Dataframe that contains the errors. Then, create table for the Dataframe using snowpark's API
B. Rely solely on Snowflake's built-in error handling mechanisms, as Snowpark automatically handles all exceptions.
C. Use Snowpark's logging API to automatically capture and log all errors without needing explicit try-except blocks. (Assume such an API exists, even if it doesn't in the current Snowpark version.)
D. Use a global try-except block to catch all exceptions and log them to the 'error_log' table.
E. Wrap individual DataFrame operations (e.g., 'filter', 'select, 'groupBy') in try-except blocks to catch exceptions specific to those operations and log them.

Question 4

You have a Snowpark DataFrame named and want to create a stored procedure that calculates the average purchase amount for each customer. The stored procedure should accept the DataFrame as input, perform the aggregation, and return a new DataFrame with the results. Which of the following code snippets BEST demonstrates how to correctly define and deploy this stored procedure?

A.

B.

C.

D.

E.

Question 5

You have developed a Snowpark application that processes a large volume of customer interaction data'. The application uses a vectorized UDF to classify the sentiment of text-based interactions. Initial tests show the application is performing slowly. Which of the following strategies would be MOST effective for optimizing the performance of sentiment analysis using a vectorized UDF?

A. Optimize the vectorized UDF code itself by reducing complex computations, memory allocations, and unnecessary function calls within the UDF function (e.g., using optimized libraries, avoiding unnecessary object creation). Also consider upgrading your pandas, scikit-learn, or transformer libraries.
B. Convert the vectorized UDF to a SQL UDTF
C. Ensure the input DataFrame is sorted by the interaction date before applying the UDF.
D. Implement mini-batching within the vectorized UDF to further optimize processing.
E. Increase the number of cores allocated to the virtual warehouse.

Solutions: