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

2. A data engineering team has developed a Snowpark Python application to process customer orders, enrich them with external data (e.g., geo location, weather) and update the Customer360 table. The application is deployed to a production environment. The application's latency has significantly increased over the last week. Your investigation reveals that the Snowflake warehouse used by the application is constantly switching between the 'Scaling Up' and 'Scaling Down' states. The team has set the Auto Suspend time to 5 minutes and Auto Resume to True. Assuming that the team hasn't changed the code, the external API or any parameter related to data ingestion, which combination of the following actions would MOST likely fix the warehouse instability issue and improve the performance of this Snowpark application in production without substantial cost increases?

A) Implement workload management and classification to ensure the Customer360 updates are prioritized over less important tasks and assigned to a dedicated resource pool.
B) Reduce the MAX CLUSTER COIJNT to limit the potential peak capacity of the warehouse, preventing excessive resource allocation.
C) Change the scaling policy of the warehouse to 'ECONOMY', prioritizing cost efficiency over performance responsiveness.
D) Increase the MIN_CLUSTER_COUNT of the warehouse. This pre-warms clusters and helps the warehouse to quickly adjust to workload changes.
E) Increase the Auto Suspend value from 5 minutes to 30 minutes. This will ensure that the warehouse remains active for a longer period, preventing frequent auto- suspends and subsequent resume operations.

3. You have a Snowpark DataFrame 'sales df with columns 'product_id' (INTEGER), 'sale date' (DATE), and 'sale_amount' (DOUBLE). You need to filter the DataFrame to include only sales that occurred in the year 2023 and where the 'sale_amount' is greater than the average 'sale_amount' for that specific 'product id'. Which of the following Snowpark Python code snippets will correctly achieve this?

A)

B)

C)

D)

E)

4. Consider the following scenario: You need to implement a UDF in Snowpark Python to calculate the distance between two geographical coordinates (latitude and longitude). The UDF should handle potential null values gracefully and return null if either input coordinate is null. Which code snippet demonstrates the MOST efficient and correct implementation, leveraging Snowpark's capabilities?

A)

B)

C)

D)

E)

5. You're working with a Snowpark DataFrame named 'sales_df' that contains sales transaction data'. You need to create a new DataFrame that includes only the rows where the 'order_date' is within the last 30 days. The 'order_date' column is currently stored as a string in 'YYYY-MM-DD' format. You want to create a schema and apply the schema to the dataframe. Choose the correct options that defines the schema in below code snippets:

A)

B)

C)

D)

E)