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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. When using key pair authentication with Snowpark, what security best practices should you implement to protect your private key?
(Select all that apply)

A) Encrypt the private key at rest.
B) Regularly rotate the key pair.
C) Store the private key directly in the Snowpark code repository.
D) Store the private key in an environment variable or a secure secret management system (e.g., HashiCorp Vault, AWS Secrets Manager, Azure Key Vault).
E) Grant broad access to the environment variable containing the private key to all developers.

2. You have a Python function named 'process data' that performs data cleaning and transformation on a Pandas DataFrame. You want to convert this function into a Snowpark Python stored procedure to leverage Snowflake's compute resources. However, the 'process_data' function relies on several external Python libraries (e.g., 'pandas', 'numpy', 'scikit-learn') that are not pre-installed in the Snowflake environment. Which of the following approaches would ensure that these dependencies are available within the Snowpark stored procedure? Choose all that apply

A) Specify the required packages in the 'packages' argument of the '@sproc' decorator or 'session.add_packageS method.
B) Include the necessary 'import' statements for the libraries within the stored procedure's code. Snowflake will automatically resolve and install the dependencies.
C) Bundle the required libraries into a ZIP file and upload it to a Snowflake stage. Then, add the ZIP file to the 'imports' list during stored procedure creation, ensuring that the library paths are correctly referenced within the Python code.
D) Create a custom Anaconda environment with the required packages and upload it to a Snowflake stage. Then, specify the stage location in the 'imports' argument of the 'CREATE PROCEDURE statement.
E) Use session.custom_package to resolve dependencied

3. You are tasked with optimizing a Snowpark application that uses a Python UDF to perform complex string manipulations on a large dataset. The current implementation uses a scalar UDF. You are considering converting it to a vectorized UDF. What are the key considerations and potential limitations you need to address during the conversion to ensure correctness and optimal performance? Choose all that apply:

A) The input and output data types of the vectorized UDF must exactly match the corresponding column data types in the Snowpark DataFrame.
B) Vectorized UDFs always perform better than scalar UDFs, regardless of the complexity of the string manipulations or the size of the dataset.
C) The vectorized UDF's return type must be compatible with Snowpark's data types, and the UDF should return an array of the appropriate type with the same length as the input arrays.
D) The vectorized UDF should utilize libraries like NumPy or Pandas for efficient array processing, but it's important to be aware of the limitations on available Python packages in the Snowflake environment.
E) The vectorized UDF must be able to handle NULL values gracefully within the input arrays, as these can cause errors if not explicitly addressed.

4. You are tasked with building a Snowpark application that receives a DataFrame 'new customers_df containing customer data'. Your application needs to insert this data into the 'CUSTOMERS' table in Snowflake. The 'CUSTOMERS table has columns 'CUSTOMER ONT), 'NAME' (VARCHAR), and 'JOIN DATE' (DATE). However, contains all columns as VARCHAR. Which of the following approaches ensures the correct data types are inserted into the 'CUSTOMERS' table, minimizing errors and maximizing performance? Assume the 'session' object is already defined and a valid connection exists.

A) Option D
B) Option E
C) Option C
D) Option A
E) Option B

5. You are working with a data science team that needs to create Snowpark DataFrames from various file types (CSV, JSON, Parquet, and XML) stored in different locations (internal stages, external stages on AWS S3, and Azure Blob Storage). The team wants a unified and reusable function to create DataFrames, abstracting away the specific file format and location details. Which of the following approaches using Snowpark Python API will provide the MOST flexible and maintainable solution?

A) Create a generic function str, file_format: str, options: dicty that uses 'getattr(session.read, file format)' to dynamically call the appropriate 'session.read' method based on the 'file_format' parameter. Pass additional configuration through the 'options' dictionary.
B) Implement a single function that uses a series of 'if/elif/else' statements to determine the file type and location, then calls the appropriate 'session.read' method with the corresponding options.
C) Create separate functions for each file type and location combination (e.g.,
D) Use the 'session.sqr method with dynamically generated SQL queries that include the file format and location details. Construct the SQL query string based on the input parameters.
E) Create a class hierarchy with an abstract base class 'DataFrameReader' that defines a 'read_file' method. Implement subclasses for each file format and location, overriding the 'read_file' method with the specific logic for that format and location.

Solutions: