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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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) Use a UDF to encrypt the 'credit_card' column before persisting the DataFrame to 'secure_customers' using
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) 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
D) Persist 'customer_df directly to 'secure_customers' using after dropping the 'credit_card' column using 'df.drop('credit_card')'.
E) 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

2. You have a Snowpark DataFrame named containing order data that needs to be inserted into the 'ORDERS table. However, due to a recent data ingestion issue, some records in might already exist in the 'ORDERS table based on the 'ORDER ID' column. Your goal is to insert only the new orders into the 'ORDERS table while avoiding duplicates. Which of the following approaches, combining efficiency and correctness, is most suitable for this task? Assume 'session' and required libraries are already imported.

A) Option E
B) Option B
C) Option C
D) Option D
E) Option A

3. You are developing a Snowpark application that uses a Python UDF to perform geocoding operations. This UDF relies on a third-party geocoding library and a large dataset of geographical data stored in a file named 'geodata.db'. The UDF needs to be operationalized with minimal latency. Which of the following strategies will result in the FASTEST execution of the UDF and optimal resource utilization?

A) Use an external function that calls a geocoding service over the internet. Store 'geodata.db' in an S3 bucket and access it from the external function. Call the external service whenever it requires it.
B) Package the geocoding library and 'geodata.db' file into a ZIP file. Upload the ZIP file to a Snowflake stage and reference it using 'imports' in the UDF definition. Use a virtual environment to manage package dependencies.
C) Create a custom Anaconda channel containing the geocoding library and 'geodata.db'. Configure the Snowflake account to use this channel. No need to use virtual environment.
D) Create a Java UDF that performs the geocoding using a Java geocoding library. Upload the JAR file and 'geodata.db' to a stage and reference them using the 'imports' clause. Java UDFs always perform faster than Python UDFs.
E) Package the geocoding library and 'geodata.db' file into a ZIP file. Upload the ZIP file to a Snowflake stage and reference it using 'imports' in the UDF definition. Ensure 'geodata.db' is loaded only once into memory per worker process using global variable and proper caching for subsequent UDF invocations. Use a virtual environment to manage package dependencies.

4. You are tasked with building a Snowpark application that receives a DataFrame 'new customers_df containing customer data'. Your application needs to insert this data into the 'CUSTOMERS' table in Snowflake. The 'CUSTOMERS table has columns 'CUSTOMER ONT), 'NAME' (VARCHAR), and 'JOIN DATE' (DATE). However, contains all columns as VARCHAR. Which of the following approaches ensures the correct data types are inserted into the 'CUSTOMERS' table, minimizing errors and maximizing performance? Assume the 'session' object is already defined and a valid connection exists.

A) Option E
B) Option B
C) Option C
D) Option D
E) Option A

5. You are working with a Snowpark DataFrame 'products_df' that contains product information, including 'product_name', 'category', and 'price'. You need to perform several transformations: 1. Rename the 'product_name' column to 'item_name'. 2. Create a new column 'discounted_price' by applying a 10% discount to the 'price' column. 3. Filter the DataFrame to only include products in the 'Electronics' category where the 'discounted_price' is less than 100. Which of the following code sequences correctly and efficiently performs these transformations in Snowpark?

A)

B)

C)

D)

E)

Solutions: