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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A data scientist has developed a Snowpark Python stored procedure named 'model_training'. This procedure utilizes a large machine learning model and requires significant compute resources. The data scientist wants to optimize the cost and performance of running this stored procedure. Which of the following strategies would be the MOST effective for achieving this goal?

A) Run the stored procedure on a larger Snowflake warehouse to reduce execution time, regardless of potential idle time.
B) Split the stored procedure into multiple smaller procedures and execute them sequentially on a smaller warehouse.
C) Convert the Python stored procedure to a SQL stored procedure to leverage Snowflake's SQL optimization engine.
D) Specify a warehouse size using the 'warehouse' parameter within the '@sproc' decorator and leverage auto-suspend and auto-resume features to minimize costs when the procedure is idle.
E) Register the stored procedure with the '@sproc' decorator without specifying any warehouse size, letting Snowflake automatically manage the warehouse.

2. You have two Snowpark DataFrames, 'customers' and 'orders'. The 'customers' DataFrame has columns and 'customer name'. The 'orders' DataFrame has columns 'order id', 'customer id', and 'order amount'. You need to find all customers who have NOT placed any orders. Which of the following Snowpark set operations correctly implements this?

A)

B)

C)

D)

E)

3. A Snowpark Python application is failing intermittently with a 'net.snowflake.client.jdbc.SnowflakeSQLException: SQL execution error: Remote service internal error [Errorld: ...l' when calling 'df.collect()' on a DataFrame that results from joining multiple tables and applying a complex filter. The data volume is substantial, but within the warehouse's expected capacity. Which of the following actions are MOST likely to resolve this issue? (Select two)

A) Increase the parameter to a higher value to prevent session timeouts.
B) Replace with 'df.toPandas(Y to improve memory management on the client side.
C) Switch to using the function with a raw SQL query instead of Snowpark DataFrame operations.
D) Implement retry logic around the 'df.collect()' call with exponential backoff, assuming the error is transient due to resource contention.
E) Break down the complex query into smaller, intermediate DataFrames and persist them using to avoid memory pressure during a single large query.

4. You are developing a Snowpark application that performs several complex transformations on a large DataFrame representing customer purchase history. This DataFrame is used multiple times in the application. You need to optimize the application's performance by caching the DataFrame. Which of the following approaches is the MOST efficient and memory-conscious way to cache the DataFrame in Snowpark?

A) Using without specifying a storage level. Snowflake will choose a default storage level.
B) Using to store the entire DataFrame in a Python list, then creating a new DataFrame from that list for each subsequent operation.
C) Using immediately after the initial DataFrame creation.
D) Using 'session.createDataFrame(df.toPandas())' to convert the Snowpark DataFrame to Pandas and back to Snowpark DataFrame.
E) Creating a temporary table in Snowflake using and then reading it back into a new DataFrame for each subsequent operation.

5. You have a Snowpark DataFrame representing customer transactions. This DataFrame is used in multiple downstream operations within your Snowpark application. Which of the following strategies would be MOST effective for optimizing the performance of these downstream operations by materializing the results of the 'df DataFrame, and what considerations should be made regarding resource usage?

A) Write the DataFrame to a persistent Snowflake table using and then read it back into a new DataFrame. This ensures data persistence but may introduce overhead due to data serialization and deserialization. Only use this method if persistence is required beyond the session.
B) Using a local variable to store the DataFrame. This method is most suitable for materializing the results of the DataFrame.
C) Use to materialize the DataFrame in memory. This is the most efficient approach as it minimizes disk I/O. Consider the size of the DataFrame relative to available memory to avoid memory pressure.
D) Create a temporary table using 'df.write.save_as_table('temp_transactions', temporary-True)'. This persists the DataFrame to Snowflake storage, reducing the need for repeated computations. Monitor the size of the temporary table and its impact on storage costs.
E) Use 'df.checkpoint()' to truncate the DataFrame lineage. This will prevent re-computation in any downstream operations. Monitor the impact on storage costs.

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