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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark Python application that interacts with Snowflake using a service account. You are rotating the private key associated with the service account. After updating the private key in your application's configuration, you encounter an error during the connection attempt: 'SnowflakeSQLException: 390103 (OSAOO): Failed to connect to DB. Encountered exception while creating connection: Authentication token has expired.' What is the MOST likely cause of this error, and what steps should you take to resolve it?

A) The Snowflake cache still holds the old private key. Clear the Snowflake connection cache in the application by calling and restarting the application.
B) The Snowflake service account hasn't been granted sufficient permissions to access the required resources. Re-grant the necessary roles and privileges to the service account.
C) The private key is in an incorrect format. Ensure that the private key is in PKCS#8 format and is properly encoded.
D) The connection string contains invalid characters. Ensure the account identifier and other parameters are correctly specified.
E) The public key associated with the new private key has not been authorized in Snowflake for the service account. Ensure that the public key is associated with the service account using ALTER SERVICE ACCOUNT SET RSA PUBLIC KEY =";'

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

3. You have a Snowpark DataFrame containing sensor data'. You need to write this data to a Snowflake stage 'sensor_stage' , creating a new set of files every hour based on the 'timestamp' column (data type: Timestamp). You also want to ensure that the file names include the hour of the timestamp and are written in Avro format with Zstandard compression. The directory structure on the stage should reflect the hourly partitioning. Which of the following approaches offers the most efficient and scalable way to achieve this, while minimizing the number of files written per hour?

A) Create a new DataFrame by adding an 'hour' column extracted from the 'timestamp' column. Then use 'df.write.partitionBy('hour').format('avro').option('compression', 'zstd').mode('append').save('@sensor_stage/')'.
B) Using scala user defined function (UDF) for write dataframe into stage in avro file format partitioned by Hour and calling it in snowpark dataframe.
C) Create a view on top of the data and schedule a task which create file in avro with zstd compression by running the select statment with group by hour.
D) Define a stored procedure that iterates through hourly intervals, filters the DataFrame based on the current hour, and writes the filtered DataFrame to the stage using 'df.write.format('avro').option('compression', 'zstd').mode('append').save(f'@sensor_stage/hour={current_hour}/')'
E) Write a Python script that connects to Snowflake, retrieves the entire DataFrame, iterates through each row, determines the hour from the 'timestamp', and writes each row to a separate Avro file named after the hour in the 'sensor_stage'

4. You have a complex data pipeline implemented using Snowpark Tasks in a Directed Acyclic Graph (DAG). One of the tasks, , depends on the successful completion of two parent tasks, and 'task B'. You need to implement error handling such that if 'task_R fails, 'task_C' should not be executed, but should still complete its execution regardless of status. If 'task B' fails, 'task_C' should not be executed. How do you configure the task dependencies and error handling in Snowflake to achieve this behavior?

A)

B)

C)

D)

E)

5. A Snowpark application connects to Snowflake using key pair authentication. After several successful executions, the application starts failing with authentication errors. You suspect an issue with the private key. Considering best practices for security and troubleshooting, which of the following actions should you take FIRST to diagnose and resolve the problem?

A) Check the Snowflake login history in ACCOUNT _ USAGE view to identify the specific error message and the IP address from which the failed login attempts originated. Use new authtype and password.
B) Immediately revoke the user's access to Snowflake and create a new user with a different private key.
C) Restart the Snowpark application server to refresh the session and clear any cached credentials.
D) Rotate the public/private key pair immediately and update the Snowpark application with the new private key. Generate new username and password.
E) Verify that the private key file exists at the specified path in the Snowpark application configuration and that the user running the application has read permissions on the file.

Solutions: