Snowflake SPS-C01 : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

You are tasked with setting up secure authentication for your Snowpark application. You want to use key pair authentication for a service user. Which of the following steps are necessary and in the correct order?

A. 1. Generate an RSA key pair (private and public key). 2. Store the private key in a database table. 3. Use database credentials in the Snowpark session configuration. 4. Associate the public key with the Snowflake user using the 'ALTER USER command.
B. 1. Generate an RSA key pair (private and public key). 2. Store the private key securely on the client machine. 3. Provide the path to the private key file and passphrase (if any) in the Snowpark session configuration. 4. Associate the public key with the Snowflake user using the 'ALTER USER command.
C. 1. Generate an RSA key pair (private and public key). 2. Store the private key securely on the client machine. 3. Associate the private key with the Snowflake user using the SALTER USER command. 4. Provide the public key in the Snowpark session configuration.
D. 1. Generate an RSA key pair (private and public key). 2. Store the public key securely on the client machine. 3. Provide the path to the public key file in the Snowpark session configuration. 4. Associate the private key with the Snowflake user using the SALTER USER command.
E. 1. Generate an RSA key pair (private and public key). 2. Store the private key securely on the client machine. 3. Provide the path to the private key file in the

Question 2

You have a Snowpark Python application that reads data from a Snowflake table, performs a complex transformation using a User- Defined Table Function (UDTF), and then writes the transformed data back to a new Snowflake table. The UDTF is defined as follows:

You need to optimize the performance of this application. Which of the following strategies would be MOST effective in reducing the execution time of the UDTF?

A. Cache the input DataFrame before applying the UDTF using 'df.cache()'.
B. Increase the warehouse size to the largest available option before running the Snowpark application.
C. Replace the UDTF with a standard User-Defined Function (UDF) as UDFs are inherently faster.
D. Reduce the number of rows in the input table by applying a filter before calling the UDTF.
E. Use a vectorized UDTF instead of a standard UDTF, processing data in batches. (Assume 'vectorized=True' is a valid parameter for 'udf .)

Question 3

Consider a Snowflake table named 'raw events' containing web event data in a VARIANT column called 'event data'. Each 'event_data' JSON object may contain an array of product IDs under the key 'product_ids', and another array of user IDs who interacted with those products under the key 'user_ids'. These arrays are not guaranteed to be the same length, and a missing array should be handled gracefully (return null/empty result for that row rather than throwing error). Your objective is to create a Snowpark DataFrame that flattens both arrays into rows, pairing each product ID with each user ID for the specific event, generating as many rows as possible, and including event timestamp. You can use a zero-based index for the array elements using 'ARRAY CONSTRUCT()' and then using that array for using the EXPLODE function. Which of the following are necessary steps to fulfill this requirement and which are not?

A. Use two separate 'withColumn()' transformations followed by two 'explode()' operations to flatten each array independently.
B. Convert the 'product_ids' and 'user_ids' arrays to strings using and before applying 'explode'.
C. Use 'iff()' or 'case when' and and other relevant function to handle exception cases.
D. Use function to dynamically create an array of indices for the 'explode' function and use and function to handle missing values
E. Use a 'LEFT OUTER JOIN LATERAL FLATTEN' to flatten 'product_ids' and 'user_ids', and then use 'nvl()' or 'coalesce()' to handle cases where one or both arrays are missing.

Question 4

You are tasked with creating a Snowpark Python stored procedure that reads data from a Snowflake table, performs a complex data transformation using a 3rd party Python library (e.g., pandas, scikit-learn), and writes the transformed data to another Snowflake table.
The data transformation requires significant memory. You need to register this stored procedure in Snowflake. Which of the following approaches is the MOST appropriate for registering the stored procedure and managing the dependencies?

A. Use the '@sproc' decorator without specifying any dependencies, assuming that the necessary libraries are pre-installed on the Snowflake worker nodes.
B. Install the required Python libraries directly on the Snowflake compute warehouse using a SQL command.
C. Create a conda environment file ('environment.yml') specifying the dependencies, upload it to a stage, and then use the '@sproc' decorator with the 'packages' argument referencing the conda environment.
D. Create a Snowflake stage, upload the Python libraries as .zip files to the stage, and specify the stage path in the '@sproc' decorator's 'imports' parameter.
E. Use the function to add the required Python libraries before registering the stored procedure with the '@sproc' decorator.

Question 5

You're developing a Snowpark Python application that reads data from a Snowflake table, performs several transformations, and writes the result to another table. You notice that the application throws a 'net.snowflake.snowpark.exceptions.SnowparkClientException: JDBC driver encountered an unexpected error.' intermittently. Examining the Snowflake query history, you observe many queries failing due to 'Warehouse Suspended'. Which of the following strategies would BEST address this issue in a production environment?

A. Implement retry logic within the Snowpark application using a try-except block and exponential backoff to handle transient connection errors.
B. Modify the Snowpark code to explicitly resume the warehouse at the beginning of the application execution and suspend it at the end.
C. Use a larger warehouse size to prevent warehouse overload during query execution, which might trigger the auto-suspension.
D. Increase the AUTO SUSPEND parameter of the warehouse to a larger value, preventing it from suspending so quickly.
E. Schedule the Snowpark application to run during off-peak hours when warehouse load is lower and less likely to be suspended.

Solutions: