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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

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

2. You have a Snowpark application that reads data from a large Snowflake table and performs several transformations. During testing, you observe that the application's performance is inconsistent, with some runs taking significantly longer than others, even with the same input data'. You suspect that data locality might be a contributing factor. What steps can you take within your Snowpark application to investigate and potentially improve data locality and performance consistency?

A) Use to redistribute the data across the cluster based on a relevant key. This can improve data locality for subsequent operations.
B) Disable Snowflake's result cache. This ensures that the application always reads the most recent data from disk, regardless of performance impact.
C) Enable Snowflake's automatic clustering on the underlying table if it's not already enabled. This will physically organize the data on disk based on the clustering key.
D) Ensure the Snowpark session is configured with a large enough warehouse size to minimize data spilling to disk.
E) Implement caching using , combined with a targeted 'repartition()' to ensure that frequently accessed data is readily available in memory close to the processing nodes.

3. Consider the following Snowpark code snippet that defines and registers a UDF:

Which of the following statements about this code are TRUE?

A) The UDF is registered as a permanent UDF and stored in the specified stage for future use.
B) The UDF is registered as a temporary UDF and will be removed when the session ends.
C) The 'replace=True' argument ensures that any existing UDF with the same name ('ADD_SALUTATION') is overwritten.
D) The 'input_types' parameter is redundant because Python's type hints are automatically used to determine the input types.
E) The default value of 'salutation' in the Python function will be used even when calling the UDF from SQL if the salutation parameter is omitted.

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 have two Snowpark DataFrames, 'dfl' and 'df2, representing customer data'. 'dfl' contains columns 'CUSTOMER ID', 'NAME, and 'EMAIL', while 'df2 contains 'CUSTOMER ID' and 'PURCHASE AMOUNT'. You need to create a new DataFrame that combines the information from both DataFrames but only includes customers who exist in BOTH 'dfl ' and 'df2 and the resulting DataFrame should have columns from both. Which of the following Snowpark DataFrame operations should you use, and what is the correct way to call it?

A)

B)

C)

D)

E)

Solutions: