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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are tasked with creating a Snowpark Python stored procedure that reads data from a Snowflake table, performs a complex data transformation using a 3rd party Python library (e.g., pandas, scikit-learn), and writes the transformed data to another Snowflake table.
The data transformation requires significant memory. You need to register this stored procedure in Snowflake. Which of the following approaches is the MOST appropriate for registering the stored procedure and managing the dependencies?

A) Create a conda environment file ('environment.yml') specifying the dependencies, upload it to a stage, and then use the '@sproc' decorator with the 'packages' argument referencing the conda environment.
B) Install the required Python libraries directly on the Snowflake compute warehouse using a SQL command.
C) Create a Snowflake stage, upload the Python libraries as .zip files to the stage, and specify the stage path in the '@sproc' decorator's 'imports' parameter.
D) Use the function to add the required Python libraries before registering the stored procedure with the '@sproc' decorator.
E) Use the '@sproc' decorator without specifying any dependencies, assuming that the necessary libraries are pre-installed on the Snowflake worker nodes.

2. You have a Python function that performs complex data transformations, too intricate to express directly in Snowpark SQL. You want to register this as a User-Defined Table Function (UDTF) so that it can be used to expand rows in a Snowpark DataFrame. The UDTF takes two arguments: an ID (integer) and a string. It returns a table with three columns: (integer), (string), and 'timestamp' (timestamp). Which of the following code snippets correctly registers this UDTF, making it available for use within Snowpark?

A)

B)

C)

D)

E)

3. Given a Snowpark DataFrame 'employees_df with columns 'employee_id' , 'department' , and 'salary' , and a second Snowpark DataFrame 'departments_df with columns 'department_id' and which of the following Snowpark code snippets correctly performs a join to retrieve employee information along with their department name, filtering for employees with salaries greater than $60,000, and then orders the result by department name?

A)

B)

C)

D)

E)

4. You are tasked with automating the creation of Snowpark sessions using key pair authentication for multiple users. You have a function that retrieves connection parameters (account, user, private key, etc.) for each user from a secure configuration file. The private keys are stored in PEM format. However, some users' private keys are password-protected. Which of the following approaches ensures the secure and correct establishment of Snowpark sessions for all users, including those with password-protected private keys? Assume get_user config(username)' retrieves the user's configuration, including the private key and password (if any).

A)

B) Attempt to establish a session without a password. If it fails, prompt the user for the password and retry the session creation using the provided password. Store the password temporarily in memory.
C) Require all users to remove the password protection from their private keys to simplify the session creation process.
D)

E) Store the password for each user's private key in a separate, encrypted file and retrieve it during session creation.

5. You are using Snowflake Notebooks to develop a Snowpark application and want to leverage a custom Python library that is not available in the default environment. What steps are necessary to make this library available within your Snowflake Notebook?

A) Install the library using pip in the Snowflake Notebook's terminal and then restart the Snowflake Notebook.
B) Create a conda environment specification file ('environment.yml') that includes the custom library, upload it to a Snowflake stage, and then create a new environment based on that file when creating or modifying the Snowflake Notebook.
C) Create a deployment file using setup.py, upload deployment file to stage, and create function
D) Install the library directly within the Snowflake Notebook using '!pip install
E) Upload the Python library's ' .py' file directly to the Snowflake stage and import it using 'import sys; sys.path.append("); import

Solutions: