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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark Python code snippet designed to create a DataFrame and then register a custom function (UDF):

This code runs successfully. However, you need to deploy this as a stored procedure. What minimal changes are required to make this code runnable as a Snowpark Python stored procedure and callable from SQL?

A) The 'return df.collect()' line must be replaced with 'return and the Snowflake session object must be explicitly passed to the UDF when it is called.
B) The 'return df.collect()' line must be replaced with 'return and 'return_type' and 'input_typeS arguments of udf must be removed. The rest of the code remains unchanged.
C) The 'return df.collect()' line must be replaced with 'return df and create a DataFrame. The rest of the code remains unchanged.
D) No changes are required; the code will function as a stored procedure as is.
E) The 'return df.collect()' line must be replaced with 'return and the 'return_type' and 'input_typeS arguments of udf must be removed to allow inference. The rest of the code remains unchanged.

2. You have a Snowpark DataFrame 'products_df with columns 'product_id', 'category', and 'price'. You want to find the top 3 most expensive products within each category Which of the following Snowpark code snippets will accomplish this, using window functions?

A)

B)

C)

D)

E)

3. You are tasked with optimizing a Snowpark application that performs sentiment analysis on customer reviews using a Python UDE The UDF uses a large pre-trained natural language processing (NLP) model stored in a file named 'sentiment_model.pkl'. The current implementation loads the model from the stage for each row of data processed, which is impacting performance. How can you optimize the application to load the model only once per worker process?

A) Use to import 'sentiment_model.pkl'. Use the decorator from the 'functools' module to cache the model loading function, initializing the model outside of the UDF definition.
B) Define 'sentiment_model.pkl' as a parameter during UDF definition to load only once per worker process and send it to the UDF.
C) Implement a custom initialization function that loads the model and is called only once per worker process. Utilize the to retrieve and cache model during session initialization. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.
D) Use a global variable to store the loaded model. Load the model from the stage into the global variable only if it is currently None. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.
E) Use the decorator from the 'functools' module to cache the model loading function. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.

4. You are developing a Snowpark application that performs complex data transformations on a large dataset using a UDF written in Scala.
After deploying the application, you observe that the performance is significantly slower than expected. Analyzing the query history in Snowflake, you identify that the UDF execution time is unusually high. Which of the following actions would be MOST effective in improving the performance of the UDF, considering Snowpark's execution context and Snowflake's query processing?

A) Modify the Scala UDF to leverage Snowpark's vectorized UDF functionality (using 'VectorizedUDF) to process data in batches instead of row-by-row.
B) Increase the warehouse size to improve overall query processing capacity, even if the UDF code itself remains unchanged.
C) Rewrite the Scala UDF using SQL stored procedure for better performance.
D) Change the UDF definition to return smaller data types if applicable.
E) Increase the amount of memory allocated to the Snowpark session in the application code.

5. Consider a Snowpark DataFrame with a containing date values, some of which are corrupted (e.g., invalid date formats or out-of-range values). You need to identify and either remove or correct these corrupted date values. Which of the following approaches can be effectively used in Snowpark Python to handle such scenarios? (Select all that apply)

A) Implement a regular expression-based filter to remove rows where the 'date_column' does not match a valid date format.
B) Use the 'to_date' function with the 'IGNORE' keyword to automatically replace invalid dates with NULL.
C) Directly replace corrupt dates using df.na.fill() after identifying the corrupt dates.
D) Use function to convert the column and filter out rows where the conversion results in NULL.
E) Define a UDF that attempts to parse the date string and returns a valid date or None if parsing fails; then, use this UDF to create a new column and filter rows with None values.

Solutions: