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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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) Disable Snowpark's lazy evaluation to ensure that all data is immediately transferred to the client for processing.
C) 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.
D) Convert all Python UDFs to SQL UDFs to leverage Snowflake's query optimizer.
E) Increase the number of worker nodes in the Snowflake virtual warehouse to improve data processing speed.

2. A data engineering team is using Snowpark Python to build a complex ETL pipeline. They notice that certain transformations are not being executed despite being defined in the code. Which of the following are potential reasons why transformations in Snowpark might not be executed immediately, reflecting the principle of lazy evaluation? Select TWO correct answers.

A) Snowpark employs lazy evaluation to optimize query execution by delaying the execution of transformations until the results are actually required.
B) Snowpark operations are only executed when an action (e.g., 'collect()', 'show()', is called on the DataFrame or when the DataFrame is materialized.
C) Snowpark automatically executes all transformations as soon as they are defined, regardless of whether the results are needed.
D) The 'eager_execution' session parameter is set to 'True'.
E) The size of the data being processed exceeds Snowflake's memory limits, causing transformations to be skipped.

3. You are troubleshooting a Snowpark application that fails to connect to Snowflake intermittently. The error message indicates an issue with the specified account identifier Which of the following actions could help resolve this issue? Select all that apply.

A) Confirm that the Snowflake service is not experiencing any outages in the specified region.
B) Ensure that the user specified in the connection parameters has the necessary privileges to access the database and schema.
C) Restart the Snowpark application server to clear any cached connection information.
D) Check the Snowflake network policy to ensure that the IP address from which the Snowpark application is connecting is allowed.
E) Verify that the account identifier is correctly specified in the connection parameters, including the region if applicable.

4. You're working with Snowpark and have a DataFrame 'df containing a column 'json_data' with JSON strings. Some of these JSON strings are invalid. You need to parse the valid JSON strings and extract a field named 'product_id' from them. Invalid JSON strings should result in a 'NULL' value for the extracted 'product_id'. Which of the following approaches is the MOST robust and efficient way to achieve this?

A)

B)

C)

D)

E)

5. You are tasked with deploying a Snowpark Python application that utilizes a third-party library, 'scikit-learn' , for machine learning tasks. The application will be executed as a Snowflake Stored Procedure. What are the necessary steps to ensure the 'scikit-learn' library is available within the Snowpark environment?

A) Install scikit-learn on your local machine, package your snowpark code into a zip file and upload it to a stage, no extra steps are required.
B) Create a Snowflake Anaconda environment using conda, include the 'scikit-learns package in the environment, and then create a Snowpark Stored Procedure that utilizes the environment via the 'packages' parameter in the CREATE PROCEDURE statement.
C) Upload the 'scikit-learrf library as a ZIP file to a Snowflake stage, create a Python UDF that unzips the library, and then import the library within the Snowpark Stored Procedure.
D) Include the 'scikit-learn' library directly in the Snowpark session using 'session.add_import(sklearny.
E) Create a Snowflake Anaconda channel integration, add the 'scikit-learn' package to the channel, and then reference the channel in the Snowpark session configuration.

Solutions: