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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:
1. You are developing a Snowpark Python stored procedure for processing financial data'. The procedure uses the 'pandas' library for data manipulation and the 'scipy' library for statistical calculations. You want to optimize the execution of the stored procedure to leverage the available resources in your Snowflake environment. Which of the following strategies would be MOST effective in improving the performance of your stored procedure, considering the need to handle large datasets?
A) Convert the Snowpark DataFrame to a Pandas DataFrame within the stored procedure and perform all calculations using Pandas and SciPy.
B) Utilize Snowpark's vectorized UDFs to perform the calculations on the data in parallel, avoiding the need to transfer the data to Pandas.
C) Use the 'cachetools' library within the stored procedure to cache intermediate results of calculations, reducing redundant computations.
D) Increase the warehouse size to the largest available option (e.g., X-Large) to provide more memory and CPU resources.
E) Partition the input DataFrame into smaller chunks and process each chunk sequentially using Pandas, then combine the results.
2. You have written a Snowpark Python function that utilizes a UDF to perform complex string manipulation on a DataFrame containing customer reviews. When deploying this function using '@sproc.test_utils.mock_snowflake environment, the test fails with a 'ModuleNotFoundError' indicating that a custom Python library (e.g., is not available. You have already confirmed that the library is installed in your local development environment. What is the MOST reliable way to ensure the UDF has access to this dependency during local testing?
A) Include the directory within the same directory as your Snowpark Python function and explicitly import it using a relative path (e.g., 'from .text_processing_lib import ...4).
B) Use before defining the UDF to explicitly include the library as a zipped file. Ensure the zipped file contains the directory.
C) Manually copy the directory into the 'ltmp' directory on your local machine before running the test. Snowpark will automatically detect libraries in this directory.
D) Append the path to the library to the 'PYTHONPATH' environment variable before executing the local tests. This will make the library available to the Snowpark session during testing.
E) Install the globally on your local machine using 'pip install
3. Consider the following Snowpark code snippet designed to create a temporary table:
A developer encounters an error when calling this function. The error message indicates that the table already exists. How should the developer modify the code to handle this scenario gracefully, preventing the error and ensuring the temporary table is either created or overwritten?
A) Add the 'mode='overwrite" option to the function. This will replace the existing table with the new data.
B) Add the 'mode='append" option to the function. This will append the data to the existing table.
C) Add the 'mode='ignore" option to the function. This will silently skip the creation if the table already exists.
D) First drop the table using 'session.sql(fDROP TABLE IF EXISTS {table_name}')' before calling .
E) Use to create the temporary table.
4. 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) 
5. You are developing a Snowpark stored procedure in Python that utilizes the 'requests' library to fetch data from an external API. Your Snowflake account is configured to use Anaconda packages. You encounter an error indicating that the 'requests' library is not found. Which of the following steps are MOST effective in ensuring the 'requests' library is available to your stored procedure?
A) Enable Anaconda integration for your Snowflake account, ensuring 'requests' is available in the Snowflake Anaconda channel, and then create the stored procedure using 'imports=['snowflake://packages/requests/']'.
B) Include the 'requests' library directly in the stored procedure code using a base64 encoded string.
C) Specify the 'requests library in the stored procedure's 'packages argument during creation: 'CREATE OR REPLACE PROCEDURE
D) Manually upload the 'requests' library's ' .py' files to an internal stage and import them within the stored procedure.
E) Install the 'requestS library directly onto the Snowflake compute nodes using SnowSQL's command.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: A,D | Question # 4 Answer: C | Question # 5 Answer: C |

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