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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have JSON files stored in an internal stage named 'json_stage' within your Snowflake account. Each JSON file contains an array of product objects, with potentially nested structures. You need to create a Snowpark DataFrame to analyze this data, but the schema is complex and you want to avoid explicitly defining it in your Python code. Which of the following Snowpark code snippets will MOST effectively achieve this, assuming you have a Snowpark session object named 'session'?

A)

B)

C)

D)

E)

2. You are developing a Snowpark Python stored procedure that needs to interact with an external REST API. The API requires authentication using an API key, which you want to store securely and access within the stored procedure. What is the MOST secure and recommended way to store and retrieve the API key within the stored procedure?

A) Store the API key as an environment variable within the Snowflake warehouse configuration.
B) Store the API Key as a comment in the Store procedure code, and retrieve it using REGEX
C) Store the API key in a Snowflake table and query it within the stored procedure.
D) Store the API key as a constant string within the stored procedure's code.
E) Store the API key in a Snowflake Secret and access it using the 'secrets' module within the stored procedure.

3. You are working with a Snowpark DataFrame 'products_df' that contains product information, including 'product_name', 'category', and 'price'. You need to perform several transformations: 1. Rename the 'product_name' column to 'item_name'. 2. Create a new column 'discounted_price' by applying a 10% discount to the 'price' column. 3. Filter the DataFrame to only include products in the 'Electronics' category where the 'discounted_price' is less than 100. Which of the following code sequences correctly and efficiently performs these transformations in Snowpark?

A)

B)

C)

D)

E)

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

5. You are setting up a development environment for Snowpark using Anaconda and encounter the following error: 'ModuleNotFoundError: No module named 'snowflake.snowpark". You have already installed the package using pip. What is the MOST likely cause of this error and how do you resolve it?

A) The 'snowflake-snowpark-python' package is not compatible with the version of Python installed in your Anaconda environment. Upgrade Python to the latest version.
B) The Snowflake account identifier is not properly configured in your environment variables. Verify that 'SNOWFLAKE ACCOUNT is correctly set.
C) The 'snowflake-snowpark-python' package was installed in a different Anaconda environment than the one you are currently using. Ensure you are in the correct environment when running your Snowpark code.
D) The Anaconda environment is not activated. Activate the environment using 'conda activate
E) The Snowflake driver is not installed. Install the Snowflake driver using 'pip install snowflake-connector-python'

Solutions: