Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Python function that performs complex data transformations, too intricate to express directly in Snowpark SQL. You want to register this as a User-Defined Table Function (UDTF) so that it can be used to expand rows in a Snowpark DataFrame. The UDTF takes two arguments: an ID (integer) and a string. It returns a table with three columns: (integer), (string), and 'timestamp' (timestamp). Which of the following code snippets correctly registers this UDTF, making it available for use within Snowpark?

A)

B)

C)

D)

E)

2. You are tasked with optimizing a Snowpark Python application that performs complex data transformations on a large dataset. The application's performance is currently bottlenecked by the data transfer between Snowflake and the client machine running the Python code. Which of the following strategies can effectively minimize data transfer and improve performance?

A) Fetch the entire dataset into a Pandas DataFrame on the client machine and perform all data transformations locally.
B) Increase the number of worker nodes in the Snowflake virtual warehouse to improve data processing speed.
C) Disable Snowpark's lazy evaluation to ensure that all data is immediately transferred to the client for processing.
D) Convert all Python UDFs to SQL UDFs to leverage Snowflake's query optimizer.
E) Use Snowpark's lazy evaluation capabilities to defer data transfer until absolutely necessary, perform data filtering and aggregation within Snowflake before bringing data to the client, and utilize vectorized UDFs for performance-critical 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. You are developing a data pipeline using Snowpark and want to optimize the execution of multiple DataFrame transformations. Which of the following strategies or techniques can you employ to improve performance and reduce execution time? (Select all that apply)

A) Using on intermediate DataFrames that are reused multiple times in subsequent transformations.
B) Leveraging Snowflake's caching mechanisms by using the 'CACHE RESULT clause after complex or frequently used queries.
C) Using to explicitly define the order in which DataFrames should be processed.
D) Eagerly evaluating all DataFrame transformations using 'df.collect()' after each transformation to materialize the intermediate results.
E) Using pushdown optimization by writing UDFs in Scala and ensuring filter operations are applied as early as possible in the data processing pipeline.

5. You are developing a Snowpark application that needs to connect to Snowflake using programmatic access. You want to use a secure method of authentication. Which of the following methods, when passed as parameters to the 'snowpark.Session.builder.configS method, would be MOST secure and appropriate for production environments?

A) Passing the 'user', 'password', and 'account' parameters directly as strings.
B) Passing the 'user' and 'password' directly, but retrieving the 'account' from an environment variable.
C) Using 'private_key' stored securely and referencing it using 'private_key_file'.
D) Using 'oauth_access_token' obtained from an external OAuth server.
E) Setting the 'authenticator' parameter to 'snowflake' and rely on default Snowflake authentication mechanism assuming it setup correctly

Solutions: