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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are working with sensor data in Snowpark. Your data contains (Integer), 'timestamp' (Timestamp), 'temperature' (Double), and 'status' (String). You need to create a Snowpark DataFrame named representing this data'. Which of the following is the most efficient and type-safe way to create the DataFrame from a list of Python tuples using an explicitly defined schema, assuming you need to maintain maximum precision for temperature readings and that all data types should map to the most efficient and appropriate Snowflake data type?

A)

B)

C)

D)

E)

2. 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) Install the required Python libraries directly on the Snowflake compute warehouse using a SQL command.
B) Use the function to add the required Python libraries before registering the stored procedure with the '@sproc' decorator.
C) 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.
D) 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.
E) Use the '@sproc' decorator without specifying any dependencies, assuming that the necessary libraries are pre-installed on the Snowflake worker nodes.

3. You have a Python function that takes a string as input and returns a sentiment score (a float between -1 and 1). This function relies on a large pre-trained Natural Language Processing (NLP) model. You want to deploy this function as a UDF in Snowpark and optimize its performance, specifically minimizing the model loading time for each execution. You have already uploaded the model to a stage named '@my_stage/models'. Select the option that combines caching techniques and UDF deployment strategies to achieve the best performance.

A) Option B and C are best and gives a scalable solution
B)

C)

D)

E)

4. You are building a Snowpark application to process sensitive data'. To enhance security, you want to leverage ephemeral sessions. Which configurations, passed to 'snowpark.Session.builder.configS , are required and sufficient to create an ephemeral session? Assume your Snowflake environment is properly configured to allow ephemeral sessions.

A) Setting the 'role' parameter to a role with appropriate privileges and 'warehouse'.
B) Setting the 'role' parameter to a role with appropriate privileges and 'database' and 'schema'.
C) Only setting the 'role' parameter to a role with appropriate privileges.
D) Setting the 'role' parameter to a role with appropriate privileges, 'database', 'schema', and 'warehouse'.
E) Ephemeral sessions do not have specific configurations; they are automatically created by Snowflake when the application connects.

5. 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)

Solutions: