Snowflake SPS-C01 Exam Overview:

Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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)

D) The code will fail because there is no call to or on the 'result_df dataframe and Snowflake performs lazy evaluation.
E) The 'result_df DataFrame will be persisted to the 'AGGREGATED SALES table in the default schema of the user running the code.

2. A data scientist has developed a Snowpark Python stored procedure named 'model_training'. This procedure utilizes a large machine learning model and requires significant compute resources. The data scientist wants to optimize the cost and performance of running this stored procedure. Which of the following strategies would be the MOST effective for achieving this goal?

A) Split the stored procedure into multiple smaller procedures and execute them sequentially on a smaller warehouse.
B) Run the stored procedure on a larger Snowflake warehouse to reduce execution time, regardless of potential idle time.
C) Convert the Python stored procedure to a SQL stored procedure to leverage Snowflake's SQL optimization engine.
D) Register the stored procedure with the '@sproc' decorator without specifying any warehouse size, letting Snowflake automatically manage the warehouse.
E) Specify a warehouse size using the 'warehouse' parameter within the '@sproc' decorator and leverage auto-suspend and auto-resume features to minimize costs when the procedure is idle.

3. You have a Snowpark application that performs machine learning inference on a large dataset of images stored in Snowflake. The inference logic is implemented within a Python UDF that utilizes a pre-trained deep learning model. You notice that the inference process is slow and consumes a significant amount of resources. Which of the following optimization techniques would be MOST effective in improving the performance and reducing the resource consumption of this application?

A) Optimize the UDF code to use batch processing techniques to process multiple images in a single call, reducing the overhead of UDF invocation.
B) Persist the pre-trained deep learning model in a database table or stage and load it into the UDF's memory during each invocation to avoid repeated loading.
C) Use external functions instead of UDFs.
D) Leverage the Snowpark Session object to manage the lifecycle and scope of the pre-trained model within the UDF, ensuring it is loaded only once per session.
E) Ensure the virtual warehouse used by Snowpark is configured with auto-scaling to dynamically adjust the compute resources based on the workload.

4. When creating UDFs/UDTFs in Snowpark Python, what are the advantages of explicitly specifying data types (either via Python type hints or the registration API) compared to relying on implicit type inference?

A) Reduced deployment time.
B) Automatic data type conversion by Snowflake, eliminating the need for explicit casting within the UDF/UDTF.
C) Early detection of type-related errors during development, preventing runtime failures.
D) Improved performance due to reduced overhead in data type resolution at runtime.
E) Enhanced code readability and maintainability, making it easier to understand the expected data types.

5. Consider the following Snowpark Python code snippet intended to operationalize a stored procedure:

This code results in an error during registration. Which of the following actions would be MOST appropriate to fix the registration issue and ensure the stored procedure can be successfully operationalized?

A) Add in sproc.register.
B) Add 'session.close()' at the end of the main function.
C) Remove the 'input_data' parameter from the 'main' function definition and access the input data directly using within the function.
D) Define 'session' as a global variable before calling 'session.sproc.register' .
E) Move the 'session.sproc.register' call outside of the 'main' function and execute it separately after the 'main' function definition.

Solutions: