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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are using VS Code with the Snowflake extension to develop a Snowpark application. You have successfully connected to your Snowflake account and are writing a script that creates a stage and then loads data from a local file into a Snowflake table using Snowpark. However, you're encountering issues with file paths and permission errors. Which of the following strategies would best address these challenges and ensure your Snowpark application can reliably load data from local files?

A) Use absolute file paths in your Snowpark code when referring to local files. Ensure the Snowflake service account has read access to the local file system.
B) Use VS Code's remote development feature to run your Snowpark code directly on the Snowflake compute nodes. This will eliminate file path issues.
C) Utilize Snowpark's 'session.file.put' to upload the local file to an internal or external stage. Then, use 'session.table.copy_into' to load data from the stage into the target table.
D) Leverage a network share and mount it as a drive in both your local development environment and the Snowflake environment. Then, use relative file paths in your Snowpark code.
E) Modify the Snowflake account-level parameters to allow direct access to the local file system. Use relative file paths to access the local file.

2. You have a Python function, 'calculate metrics(df: snowpark.DataFrame, metric name: str) -> snowpark.DataFrame', that calculates various metrics on a Snowpark DataFrame. You want to deploy this function as a stored procedure in Snowflake. You need to ensure that the stored procedure has appropriate permissions to read data from a table named 'customer data' and write results to a table named 'metrics_table'. Which of the following steps are necessary to achieve this, assuming you are using the 'session.sproc.register' method?

A) Specify the 'packages' argument in 'session.sproc.register' to include any Python dependencies required by the 'calculate_metrics' function.
B) Grant the 'SELECT privilege on the 'customer_data' table and the 'INSERT privilege on the 'metrics_table' table to the role executing the stored procedure.
C) Grant the 'USAGE privilege on the database and schema containing the 'customer_data' and 'metrics_table' tables to the role executing the stored procedure.
D) Specify the 'imports' argument in 'session.sproc.register' with the list of packages which are needed to run 'calculate_metrics' function.
E) When registering the stored procedure using 'session.sproc.register' , specify the argument and provide a 'replace=True' if necessary. This will allow you to assign ownership of the stored procedure to a role with the necessary privileges.

3. You have a Snowpark DataFrame named 'sales df that contains daily sales data'. You need to calculate the weekly sales for each product and store the results in a new DataFrame. The calculation of weekly sales involves a window function that is computationally expensive. To optimize performance, you decide to cache the DataFrame after applying the window function. However, after implementing the caching, you notice that the performance is not improved as expected. What could be the reason for this and how can you fix it?

A) The DataFrame is being evicted from the cache due to memory pressure. Increase the warehouse size or reduce the data being processed.
B) Snowflake automatically optimizes window function calculations, rendering explicit caching unnecessary.
C) The window function is not cacheable. Window functions cannot be cached using 'cache_result()'.
D) The DataFrame is too small. Caching only benefits large DataFrames.
E) The call is placed before the window function. Move the call after applying the window function.

4. You are tasked with optimizing a Snowpark application that processes sensor data'. The data includes timestamp, sensor ID, and sensor reading. Your initial implementation uses a regular Python UDF to calculate the moving average for each sensor. However, the processing time is significantly slow due to the large volume of data'. Which of the following strategies would be MOST effective in improving the performance of this calculation using vectorization?

A) Increase the warehouse size without modifying the UDF code.
B) Convert the Python UDF to a Java UDF.
C) Replace the Python UDF with a SQL UDF as SQL UDFs are inherently faster.
D) Rewrite the calculation logic using Snowpark's built-in aggregation functions instead of a UDF.
E) Convert the existing Python UDF into a vectorized UDF using the '@vectorized' decorator, ensuring the input and output are Pandas Series.

5. You've created a Snowpark Python stored procedure designed to perform sentiment analysis on customer reviews stored in a Snowflake table. This procedure utilizes a third-party Python library, 'transformers', for its sentiment analysis model. You need to operationalize this stored procedure for scheduled execution. Which of the following options represents the MOST efficient and reliable approach for deploying and managing the 'transformers' dependency within the Snowflake environment for your stored procedure, minimizing deployment complexity and potential runtime errors?

A) Install the 'transformers' library on the Snowflake compute warehouse nodes directly using a startup script.
B) Include the 'transformers' library as a part of the task definition and make it available for the stored procedure that the task is invoking.
C) Create a Snowflake Anaconda channel, upload the 'transformers' library to this channel, and specify the channel in the stored procedure definition using the packageS parameter.
D) Upload the 'transformers' library as a zip file to a Snowflake stage and reference it in the 'imports' parameter when creating the stored procedure.
E) Include the 'transformers' library directly within the stored procedure's Python code as a string literal and execute it using or 'eval()'.

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