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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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) 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.
B) 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.
C) 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.
D) 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.
E) 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.

2. You are tasked with creating a Snowpark DataFrame from a Python list of tuples. Each tuple represents a customer record with the following structure: '(customer_id, signup_date, The 'customer _ id' should be an integer, 'signup_date' should be a date, and should be a decimal. You want to define the schema explicitly for type safety and performance. Which of the following code snippets correctly defines the schema and creates the Snowpark DataFrame?

A)

B)

C)

D)

E)

3. Consider the following Snowpark Python code snippet designed to calculate the moving average of sales data'. You've identified that the code is performing poorly and suspect the window function is a bottleneck. How can you optimize this code for better performance?

A) Explicitly specify a range-based window frame (e.g., 'rowsBetweeri) instead of a rows-based window frame (e.g., 'rangeBetweeri) if appropriate for the calculation.
B) Ensure that the data is pre-sorted according to the ordering specified in the window function before creating the Snowpark DataFrame.
C) Use the method on the Snowpark DataFrame before applying the window function to avoid re-reading the data multiple times.
D) Rewrite the window function logic as a series of aggregation queries to improve performance on very large datasets.
E) Replace the 'Window.orderBy' with 'Window.partitionBV on a highly cardinal column to distribute the window calculations across multiple nodes.

4. You have a Snowpark DataFrame containing sales data with columns 'region' , and 'sales_amount'. You need to calculate the total sales amount for each region and then filter the results to only include regions where the total sales amount is greater than 10000. Which of the following Snowpark code snippets correctly implements this logic?

A)

B)

C)

D)

E)

5. You are tasked with creating a Snowpark stored procedure that needs to access a secret stored in Snowflake's Secret Managen The secret contains credentials required to connect to an external API. Which of the following steps are necessary to correctly and securely access and use the secret within your Snowpark stored procedure? (Select all that apply)

A) Use the method within the stored procedure to retrieve the secret value.
B) Create a UDF that exposes the secret and call that UDF in the stored procedure.
C) Grant the USAGE privilege on the secret to the role that will execute the stored procedure.
D) Ensure that the stored procedure is created with the 'EXECUTE AS CALLER clause.
E) Store the secret value directly in the stored procedure's code as a global variable.

Solutions: