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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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) Use a single Snowpark session object and dynamically update the connection parameters (account identifier, username, password) whenever switching to a different account and region.
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 a new Anaconda environment for each Snowflake account and install the necessary packages in each environment.
E) Create separate Python scripts for each account and region, hardcoding the account identifiers and credentials within each script.

2. A Snowpark application needs to dynamically switch between different Snowflake accounts based on the environment (development, staging, production). Which of the following approaches provides the MOST secure and maintainable way to manage account credentials without hardcoding them in the application? Assume that deployment will occur via docker, Kubernetes or other modern deployment practices.

A) Encrypt the credentials and store them in a configuration file that is decrypted at runtime using a key stored in a secure vault.
B) Store credentials in environment variables managed by the deployment platform (e.g., Kubernetes secrets) and access them using
C) Hardcode credentials in the Snowpark application code and rely on network security to prevent unauthorized access.
D) Store credentials in separate .env' files for each environment and load the appropriate file based on an environment variable indicating the current environment.
E) Use the Snowflake CLI configuration file ('-/.snowflake/config') and switch between named profiles based on an environment variable.

3. You have a Snowpark DataFrame 'df' containing customer data with columns 'customer id', 'name', 'age', and 'city'. You want to filter the DataFrame to include only customers from 'New York' who are older than 30, then extract the 'customer id' and 'name' into a Rows object, and finally print the 'name' of the first row in the Rows object. Which of the following code snippets correctly achieves this using Snowpark Python?

A)

B)

C)

D)

E)

4. You are developing a Snowpark application that uses a UDTF written in Python to perform complex data transformations. The UDTF takes several input columns and returns multiple output columns. The data volume is very large. You observe performance bottlenecks during the UDTF execution. Which of the following strategies could you employ to optimize the performance of your UDTF? (Select TWO)

A) Reduce the number of input columns passed to the UDTF by performing some pre-processing outside the UDTF.
B) Avoid using UDTFs altogether and rewrite the transformation logic using built-in Snowpark DataFrame transformations, even if it makes the code significantly more complex.
C) Use a scalar UDF instead of a UDTF to simplify the code and reduce overhead.
D) Increase the warehouse size used for the Snowpark session to provide more computational resources.
E) Employ vectorized operations within the UDTF using libraries like NumPy or pandas to process data in batches.

5. Consider the following Snowpark Python code snippet that retrieves data and calculates aggregate values, however, the application performance is slow when you are fetching dataframe, given the 'block' parameter controls the synchronous/asynchronous behavior of the 'collect()' method. Choose ALL the statements about "session.create_dataframe([rowl ,row2],schema)' that are correct:

A) Consider replacing the call of 'collect()' with and converting Snowpark dataframe to Pandas dataframe. The dataframe operations could be efficient.
B) The method, when executed with default 'block=True' , will wait for the result to be available before proceeding to the other statements in the code and hence performance is low. However, makes it asynchronous.
C) Snowflake warehouse size has no influence on dataframe creation, so if the dataframe is large and 'block=True' it will block the performance.
D) The 'block' parameter has no influence on performance when creating dataframes, and hence the performance is mainly based on query optimization and ware house size.
E) Increasing warehouse size of Snowflake will increase performance irrespective of the fact whether block is True or False.

Solutions: