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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider a DataFrame 'products df loaded from a SnoMlake table. It contains a 'features' column of type VARIANT, where each row contains a JSON object representing product features. Your task is to create a new DataFrame where each feature becomes a separate column. You need to dynamically extract these features without knowing the specific feature names in advance. Which of the following approaches could achieve this using Snowpark, and what considerations are important? Choose all that apply:

A) Use a User-Defined Function (UDF) to parse the JSON and return a dictionary. Then, use a loop to iterate over the keys in the dictionary and create new columns based on these keys.
B) Use the function on the VARIANT column to get an array of feature names. Then, use a loop to iterate over this array and dynamically create new columns using bracket notation (e.g.,
C) The function can be used in conjunction with a Snowpark SQL query to dynamically extract the json into separate columns.
D) It's not possible to dynamically extract feature names and create columns in Snowpark without knowing the schema in advance.
E) Use the 'FLATTEN' function within a Snowpark DataFrame transformation. This allows you to transform the key-value pairs within the VARIANT column into separate rows, which can then be pivoted to create new columns.

2. Consider the following Snowpark Python code snippet that defines and applies a UDF:

Which of the following modifications would MOST likely improve the performance of this code, assuming the DataFrame 'df contains a large number of rows?

A) Use and F.lit(0.2Y instead of 0.1 and 0.2 while creating the dataframe.
B) Specify a different warehouse size when creating the Snowpark session using 'session = Session.builder.config('warehouse', 'XLARGE').configs(connection_params).create()'.
C) Rewrite the 'apply_discount' function to use NumPy arrays internally for vectorized calculations, ensuring compatibility with vectorized UDF execution. The function signature will also need to accept arrays.
D) Change to in the "session.udf.register' call, ensuring the function is updated to handle batches of data.
E) Remove the 'input_types' argument from 'session.udf.register' . Snowflake can automatically infer the input types.

3. You are developing a secure UDF in Snowpark Python that needs to access sensitive data stored in an internal stage. The UDF should be accessible to users without granting them direct access to the stage. Which of the following security measures and code snippets are required to achieve this, assuming the stage is already created?

A) Create a secure UDF and use the function to access stage credentials within the UDF's handler function.
B) Create a secure UDF and use a stored procedure owned by a role with access to the internal stage to retrieve data, passing the data to the UDF as an argument.
C) Create an external function and grant access to the API integration that provides the security context.
D) Create a UDF and grant USAGE on the stage to the role that owns the UDF.
E) Create a secure UDF using the ' VOLATILE keyword, allowing it to access secured data with current user's permissions.

4. A data engineer is tasked with creating a Snowpark Python application that needs to access data from multiple Snowflake accounts and regions. All accounts are using Snowflake's Business Critical edition. Which of the following approaches would be the MOST efficient and maintainable for managing and switching between different Snowpark sessions in this scenario?

A) Use the 'snowflake.connector.connect' method to directly establish connections without using the Snowpark Session object.
B) Create a new Anaconda environment for each Snowflake account and install the necessary packages in each environment.
C) Create a configuration file (e.g., YAML or JSON) that stores the account identifiers and other connection details for each Snowflake account and region. Load this configuration and create separate Snowpark session objects for each account, storing them in a dictionary or list.
D) Create separate Python scripts for each account and region, hardcoding the account identifiers and credentials within each script.
E) Use a single Snowpark session object and dynamically update the connection parameters (account identifier, username, password) whenever switching to a different account and region.

5. You are tasked with creating a secure UDF in Snowflake using Snowpark Python API that encrypts sensitive customer data before storing it. The UDF should be accessible only to users with specific roles. You have the following code snippet. What needs to be done to make it secure and operationalize for multiple users?

Considering Security best practices, what steps are critical for securing the UDF and granting appropriate permissions to other users?

A) Use Snowflake's Secret object to securely store the encryption key and retrieve it within the UDF. Grant USAGE privilege on the secret to specific roles.
B) Grant global USAGE privilege on the function's stage location to all users.
C) Grant EXECUTE privilege on the UDF and USAGE privilege on the database and schema containing the UDF to the roles needing access.
D) Bypass role-based access control and use shared credentials for UDF execution.
E) Store the encryption key directly in the UDF code for simplicity.

Solutions: