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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark Python stored procedure that performs complex data transformations. This stored procedure needs to read data from a large table ('TRANSACTIONS) and write the transformed data to another table PROCESSED TRANSACTIONS'). You want to optimize the performance of this stored procedure by leveraging Snowpark's features for parallel processing. Which of the following approaches can significantly improve the performance of the stored procedure, assuming sufficient warehouse resources are available?

A) Load the data from 'TRANSACTIONS' table into a temporary table within the stored procedure, then use standard SQL queries on the temporary table for transformations, finally using snowpark DataFrame API to write it back to the 'PROCESSED_TRANSACTIONS' table.
B) Read the entire 'TRANSACTIONS table into a Pandas DataFrame within the stored procedure and perform the transformations using Pandas functions. Then, write the transformed data back to the table using Snowpark's 'createDataFrame' and 'write' methods.
C) Use the Snowpark DataFrame API to read the 'TRANSACTIONS' table and apply transformations using vectorized UDFs. Then, use the 'write' method to write the transformed data to the 'PROCESSED TRANSACTIONS' table.
D) Use Snowpark's 'sprocs decorator with appropriate 'packages' and leverage the Snowpark DataFrame API with vectorized UDFs to transform the data. Use 'session.write_pandaS to write the Pandas DataFrame to the 'PROCESSED_TRANSACTIONS' table after the transformation.
E) Use Snowflake's standard SQL queries within the stored procedure to read and transform the data. Write the results to the 'PROCESSED TRANSACTIONS table using 'INSERT statements.

2. You are tasked with building a machine learning pipeline in Snowpark to predict customer churn. You plan to use the scikit-learn library for model training and want to deploy the trained model as a Snowpark UDF for real-time scoring. Consider the following code snippet:

A) The code will fail because the return type of the UDF is not explicitly defined. Snowpark requires explicit type hints for UDF return values.
B) The code will execute successfully and create a UDF that predicts churn using the trained model.
C) The code will fail because scikit-learn is not a supported library for Snowpark UDFs by default, and needs to be explicitly added to the session imports.
D) The code will fail because the 'moder object cannot be directly serialized and passed to the UDE A different serialization method (e.g., pickle) is required.
E) The code will fail because the trained model needs to be saved to a stage and loaded within the UDF, rather than being passed directly.

3. A data engineering team has deployed a Snowpark Python application that reads data from a Snowflake table, performs several complex transformations using Snowpark DataFrames, and writes the results back to another Snowflake table. The team is concerned about the cost associated with the virtual warehouse used by the Snowpark application. Which of the following strategies would be MOST effective in minimizing the virtual warehouse costs while maintaining acceptable performance?

A) Optimize the Snowpark code to minimize data shuffling and reduce the amount of data processed.
B) Set the AUTO SUSPEND parameter of the virtual warehouse to the shortest possible duration (e.g., 60 seconds).
C) Use serverless compute service when possible to avoid managing warehouse.
D) Implement a resource monitor to limit the credit consumption of the virtual warehouse used by the Snowpark application.
E) Use the smallest possible virtual warehouse size (e.g., X-SMALL) and rely on Snowflake's automatic scaling capabilities to handle workload spikes.

4. You are optimizing a Snowpark application that performs complex data transformations on a large dataset. The transformation involves multiple joins and aggregations. You notice that the query execution time is excessive. Which of the following techniques would be MOST effective in improving the performance of this application, assuming you have the appropriate Snowflake role and privileges?

A) Use the ' DataFrame.explain(Y method to analyze the query plan and identify potential bottlenecks. Optimize the code based on the query plan analysis, focusing on reducing data shuffling and improving join strategies.
B) Reduce the overall complexity of the query, but increase the number of queries being executed. This allows for a more incremental approach.
C) Increase the size of the Snowflake warehouse used for the computation. A larger warehouse provides more resources for parallel processing.
D) Disable result caching for the Snowflake session. This ensures that the most up-to-date data is always used, even if it slows down performance.
E) Rewrite the Snowpark code to use more UDFs (User-Defined Functions). UDFs are always more efficient than built-in functions.

5. You have a Snowpark DataFrame 'df representing sales data with columns 'product_id', 'region', and 'sales_amount'. You want to calculate the total sales amount for each region. Which of the following Snowpark code snippets is the MOST efficient and correct way to achieve this?

A)

B)

C)

D)

E)

Solutions: