Snowflake Certified SnowPro Specialty - Snowpark : SPS-C01

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Python UDTF that calculates a running average from a stream of numerical data'. The UDTF's 'process' method maintains state (the running sum and count) between calls. You need to ensure that the UDTF's state is properly initialized for each new group of data processed within a Snowpark DataFrame. What are the requirements?

A) The UDTF class must define a 'reset' method. This method will be called by Snowpark at the beginning of processing each group of rows.
B) The UDTF class must define a '___init___' method to initialize the state variables and also 'reset method. This '___init____' and 'reset methods will be called once per UDTF instance.
C) The UDTF class must define a '___del___' method. This method will be called by Snowpark at the beginning of processing each group of rows.
D) The UDTF class must define a 'end_partition' method to finalize processing and avoid memory leaks.
E) The UDTF class must have an '__init____' method to initialize the state variables. This '___init____' method will be called once per UDTF instance.

2. You're developing a Snowpark application that reads data from a Snowflake table, performs several transformations, and then writes the results back to a different table. You want to ensure that the entire process is executed as a single atomic transaction, even if it involves multiple Snowpark DataFrames and operations. Which of the following actions are required to achieve this transactional behavior?

A) Explicitly start a transaction using 'session.beginTransaction()' at the beginning of the Snowpark application and commit it using 'session.commitTransaction(Y at the end.
B) Leverage the 'CREATE OR REPLACE TABLE AS SELECT statement within a Stored Procedure called from your Snowpark code. All DML operations done as part of stored proc is transactional
C) Configure the Snowpark session with the parameter set to ' FALSE
D) All Snowpark operations within a single session are automatically executed as a single atomic transaction by default; no additional configuration is required.
E) Ensure that the target table for writing the results has the 'TRANSIENT' property set to 'TRUE'.

3. You have a Snowflake stage containing image files. You need to write a Snowpark Python application that extracts metadata (e.g., image resolution, format) from these images and stores the metadata in a Snowflake table. You want to leverage a Python library, such as Pillow (PIL), for image processing. Which of the following steps are necessary to correctly and efficiently implement this?

A) Create a Python UDF (User-Defined Function) that uses Pillow to extract metadata from the image files. Register the UDF with Snowflake. In a Snowpark DataFrame transformation, call the UDF for each image file to extract the metadata. Finally, write the resulting DataFrame to a Snowflake table.
B) Upload the Pillow library as a zip file to a Snowflake internal stage. Create a Snowpark stored procedure. In the stored procedure code, import the Pillow library using 'import zipfile; sys.path.append('pillow.zip'); from PIL import Image'. Read the image files using , process them with Pillow to extract metadata, and then insert the metadata into the Snowflake table.
C) Use Snowpark's built-in image processing functions to extract metadata directly from the image files. This eliminates the need for external libraries like Pillow.
D) Download all the image files to the Snowpark client, process them locally using Pillow, and then upload the extracted metadata to Snowflake using session
E) Create a Conda environment specification file ('environment.yml') that includes Pillow as a dependency. Upload the 'environment.ymr file to a Snowflake stage. Use 'session.add_packages' in the Snowpark session to load the Pillow library. Read the image files using , process them with Pillow, and then write the metadata to a Snowflake table using 'session.write_pandas()'.

4. You are building a Snowpark application that requires you to connect to Snowflake from an environment where directly specifying credentials in the code is not permitted for security reasons. Which of the following are valid and recommended ways to securely pass authentication information to the Snowpark Session?

A) Using environment variables and retrieving them using 'os.environ' to build the connection parameters. This is a secure and recommended approach.
B) Storing credentials in a Snowflake stage and retrieving them from there at runtime. This is an acceptable, though more complex, solution.
C) Hardcoding the credentials in the Snowpark Python script and obfuscating them using Base64 encoding. This provides security by obscurity, making it a reasonably secure approach.
D) Using the Snowflake CLI's 'snowflake configure' command and relying on the A.snowflake/config' file. This is suitable for development but not recommended for production due to local file dependency.
E) Storing credentials in a dedicated secret management service (e.g., HashiCorp Vault, AWS Secrets Manager) and retrieving them using an appropriate API. This is the most secure and recommended approach for production environments.

5. You have a Snowpark DataFrame 'df representing sales data with columns 'product_id', 'region', and 'sales_amount'. You want to calculate the total sales amount for each region. Which of the following Snowpark code snippets is the MOST efficient and correct way to achieve this?

A)

B)

C)

D)

E)

Solutions: