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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

A Snowpark application is designed to process data residing in a Snowflake table called 'ORDERS'. The application needs to create a temporary view named 'TEMP ORDERS VIEW based on a filtered subset of this table. The view should only be accessible within the current Snowpark session and should be automatically dropped when the session ends. What is the MOST efficient and correct Python code snippet using Snowpark to achieve this?

A.

B.

C.

D.

E.

Question 2

You are developing a Snowpark stored procedure in Python that needs to access and modify a temporary table within the same session.
Which of the following approaches is the MOST efficient and recommended way to achieve this?

A. Using to create a DataFrame representing the temporary table, and then performing all operations using DataFrame transformations.
B. Using the Python DB API (e.g., 'snowflake.connector' ) within the stored procedure to establish a separate connection to Snowflake and interact with the temporary table.
C. Creating a global temporary table using 'CREATE GLOBAL TEMPORARY TABLE outside the stored procedure and then accessing it within the stored procedure using 'session.table()'.
D. Using 'session.createOrReplaceTempView()' to create a temporary view based on a DataFrame, and then querying the view using 'session.sql('SELECT
E. Using 'session.sql('CREATE TEMPORARY TABLE followed by subsequent 'session.sql('lNSERT INTO and 'session.sql('SELECT statements to interact with the temporary table.

Question 3

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. 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.
C. Create separate functions for each file type and location combination (e.g.,
D. 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.
E. 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.

Question 4

You are tasked with setting up Snowpark sessions using environment variables defined in a .env' file. You have successfully installed the 'python-dotenv' package and configured your .env' file with the necessary Snowflake connection parameters. However, when your Snowpark application attempts to create a session, it fails with a connection error. Which of the following could be the possible reasons for the failure, assuming you are correctly using 'os.getenv' to access the environment variables?

A. The .env' file is not located in the same directory as the Python script.
B. The Snowflake account identifier specified in the ' .env' file is incorrect or inaccessible from the network where the Snowpark application is running.
C. The warehouse defined in your session creation code does not exist or the role defined in the 'snowflake.connector.connect' does not have appropriate warehouse privileges.
D. The required environment variables (e.g., 'SNOWFLAKE_USER, SNOWFLAKE_PASSWORD, 'SNOWFLAKE_ACCOUNT) are not defined or are incorrectly named in the ' .env' file.
E. The 'python-dotenv' package was installed, but the ' .env' file wasn't loaded by calling before creating the session.

Question 5

You have a Snowpark DataFrame with columns 'sale_date', 'product_id', and 'revenue'. You need to calculate the cumulative revenue for each product over time. Which of the following approaches will accomplish this in Snowpark using window functions?

A.

B.

C.

D.

E.

Solutions: