Free Access Snowflake.SPS-C01.v2026-07-23.q130 Practice Test (Page 26)

Question 121

A Snowpark application processes streaming data from Kafka, performing complex windowing aggregations. The application is configured with auto-scaling enabled for the virtual warehouse. During peak hours, the application exhibits high latency despite the warehouse scaling up. Upon investigation, you observe sustained high CPU utilization on the single active warehouse. Which actions, alone or in combination, would MOST effectively improve performance while minimizing cost?

A.Increase the MAX CLUSTER COUNT parameter for the virtual warehouse. This ensures that the warehouse can scale out to a greater number of clusters to handle the increased workload.

B.Optimize the Snowpark code by using vectorization and efficient data structures. This reduces the CPU load for each processing task.

C.Repartition the input data to distribute the workload more evenly across the available clusters. Ensure the partitioning key is suitable for the aggregations being performed.

D.Increase the MIN_CLUSTER_COUNT parameter to pre-warm additional clusters. This ensures that clusters are readily available when the workload increases, reducing latency.

E.Decrease the SCALING_POLICY parameter to reduce the time it takes for warehouses to autoscale. This will allow warehouses to keep up with processing as volume increases.

Question 122

You are developing a Snowpark application that needs to access data from a Snowflake table called 'EMPLOYEES. You want to create a Snowpark DataFrame representing this table. However, you are facing issues with the connection and believe that the database, schema, or warehouse attributes may not be set up correctly for the session. Which of the following code snippets, used in conjunction, BEST demonstrates how to create a Snowpark session with robust error handling to identify and address potential connection issues before attempting to create the DataFrame?

Question 123

You are building a Snowpark application that uses a Python UDF to perform sentiment analysis on customer reviews. The UDF relies on a large pre-trained machine learning model loaded from a file. During execution, you encounter 'Out of Memory' errors within the UDF. Considering the constraints of the Snowpark execution environment and the need to optimize resource usage, which of the following steps is the MOST effective in addressing this issue and ensuring the application's stability and performance?

A.Increase the overall size of the Snowflake warehouse to provide more memory for UDF execution. The Snowflake environment will automatically allocate more memory to UDFs when available.

B.Implement lazy loading of the machine learning model within the UDF, ensuring that the model is loaded only when it's first needed, and then cached for subsequent calls within the same UDF invocation.

C.Break down the customer reviews into smaller chunks and process them in batches within the UDF, clearing the model from memory after each batch to reduce overall memory consumption.

D.Use Snowpark's 'sproc' to register the UDF as a stored procedure instead ofa UDF, as stored procedures typically have more memory allocated to them.

E.Optimize the model itself by reducing it's size through quantization or distillation, and re-upload the smaller model to the Snowflake stage for the UDF to use.

Question 124

You are tasked with automating the creation of Snowpark sessions using key pair authentication for multiple users. You have a function that retrieves connection parameters (account, user, private key, etc.) for each user from a secure configuration file. The private keys are stored in PEM format. However, some users' private keys are password-protected. Which of the following approaches ensures the secure and correct establishment of Snowpark sessions for all users, including those with password-protected private keys? Assume get_user config(username)' retrieves the user's configuration, including the private key and password (if any).

A.Attempt to establish a session without a password. If it fails, prompt the user for the password and retry the session creation using the provided password. Store the password temporarily in memory.

B.Require all users to remove the password protection from their private keys to simplify the session creation process.

D.Store the password for each user's private key in a separate, encrypted file and retrieve it during session creation.

Question 125

You have a Snowpark Python application that uses a UDF to perform custom data transformations. The UDF relies on a large, read-only lookup table that is stored as a CSV file on a Snowflake stage. Which of the following strategies would be the MOST efficient way to access the lookup table within the UDF?

A.Read the CSV file from the stage every time the UDF is called using 'snowflake.connector.connect()' and then load the data into a Pandas DataFrame within the UDF function.

B.Read the CSV file from the stage once when the UDF is first called, cache the data in a global variable within the UDF module, and then reuse the cached data for subsequent calls.

C.Load the CSV file into a Snowflake table and then query the table from within the UDF using 'session.sql(V.

D.Load the CSV file into a Snowflake stage, and in the python UDF code, use the get_stage_file API from session object to read the file once. Then the data cached in-memory within the UDF module, and reuse the cached data for subsequent calls.

E.Use the 'cachetoolS library with a Least Recently Used (LRU) cache to store the lookup table in memory. The UDF will check the cache before reading the CSV file, and update the cache if necessary. The CSV file is read with get_stage_file API from session.

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