Snowflake SPS-C01 Exam Questions : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark Python code snippet:

A) The code will fail because there is no call to or on the 'result_df dataframe and Snowflake performs lazy evaluation.
B) This code will fail because is not a valid method for Snowpark DataFrames.
C) The 'result_df DataFrame will be persisted to the 'AGGREGATED SALES table in the default schema of the user running the code.
D) This code will ovemrite the table if it already exists.
E)

2. You are developing a Snowpark Python stored procedure for processing financial data'. The procedure uses the 'pandas' library for data manipulation and the 'scipy' library for statistical calculations. You want to optimize the execution of the stored procedure to leverage the available resources in your Snowflake environment. Which of the following strategies would be MOST effective in improving the performance of your stored procedure, considering the need to handle large datasets?

A) Use the 'cachetools' library within the stored procedure to cache intermediate results of calculations, reducing redundant computations.
B) Increase the warehouse size to the largest available option (e.g., X-Large) to provide more memory and CPU resources.
C) Convert the Snowpark DataFrame to a Pandas DataFrame within the stored procedure and perform all calculations using Pandas and SciPy.
D) Utilize Snowpark's vectorized UDFs to perform the calculations on the data in parallel, avoiding the need to transfer the data to Pandas.
E) Partition the input DataFrame into smaller chunks and process each chunk sequentially using Pandas, then combine the results.

3. You have a Snowpark Python stored procedure 'process_data' that takes a Snowpark DataFrame as input, performs several data transformations using functions defined in a separate Python module 'data utils.py', and returns a transformed DataFrame. The 'data utils.py' file is located in your local directory. You want to register this stored procedure so that it can be called from Snowflake. Which of the following code snippets demonstrate(s) the correct way to register the stored procedure, ensuring that the 'data utils.py' module is available within the Snowpark environment? (Select TWO)

A)

B)

C)

D)

E)

4. You have a Snowpark DataFrame named 'customer df containing customer data, including sensitive information like credit card numbers in a column named 'credit card'. You need to persist this data to a Snowflake table named 'secure_customers'. What is the MOST secure and efficient way to achieve this, ensuring that the 'credit card' column is never exposed in plain text during the persistence process and also optimized for subsequent analytical queries?

A) Persist 'customer_df directly to 'secure_customers' using after dropping the 'credit_card' column using 'df.drop('credit_card')'.
B) Persist the 'customer_df to a temporary table using 'df.write.mode('overwrite').save_as_table('temp_customers')'. Then, create a new table 'secure_customers' from 'temp_customers' excluding the 'credit_card' column.
C) Use a UDF to encrypt the 'credit_card' column before persisting the DataFrame to 'secure_customers' using
D) Create a masking policy in SnoMlake and apply it to the 'credit_card' column in the'secure_customers' table after persisting the 'customer_df using
E) Create a Snowpark DataFrame that uses a Secure View to only select the required columns excluding credit_card, and persist that to 'secure_customers' using

5. You have developed a Snowpark application that processes a large volume of customer interaction data'. The application uses a vectorized UDF to classify the sentiment of text-based interactions. Initial tests show the application is performing slowly. Which of the following strategies would be MOST effective for optimizing the performance of sentiment analysis using a vectorized UDF?

A) Implement mini-batching within the vectorized UDF to further optimize processing.
B) Convert the vectorized UDF to a SQL UDTF
C) Ensure the input DataFrame is sorted by the interaction date before applying the UDF.
D) Increase the number of cores allocated to the virtual warehouse.
E) Optimize the vectorized UDF code itself by reducing complex computations, memory allocations, and unnecessary function calls within the UDF function (e.g., using optimized libraries, avoiding unnecessary object creation). Also consider upgrading your pandas, scikit-learn, or transformer libraries.

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