Snowflake SPS-C01 : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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) Ensure that the target table for writing the results has the 'TRANSIENT' property set to 'TRUE'.
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) Explicitly start a transaction using 'session.beginTransaction()' at the beginning of the Snowpark application and commit it using 'session.commitTransaction(Y at the end.
D) All Snowpark operations within a single session are automatically executed as a single atomic transaction by default; no additional configuration is required.
E) Configure the Snowpark session with the parameter set to ' FALSE

2. You are tasked with creating a Snowpark session that utilizes a specific Snowflake warehouse for all operations. Which of the following code snippets BEST demonstrates how to correctly specify the 'warehouse' parameter when creating a session using snowpark.Session.builder.configs'?

A)

B)

C)

D)

E)

3. You are tasked with developing a data pipeline using Snowpark that involves reading data from multiple CSV files, performing transformations using Pandas DataFrames, and then loading the transformed data into a Snowflake table. You want to optimize the process by leveraging the capabilities of Snowpark and Pandas effectively. Which of the following approaches is the MOST efficient for creating the Snowpark DataFrame from the pandas dataframe? (Select all that apply.)

A) Read each CSV file into a Pandas DataFrame, perform transformations, concatenate all Pandas DataFrames into a single Pandas DataFrame, and then create a Snowpark DataFrame using 'session.createDataFrame()'.
B) Read each CSV file directly into a Snowpark DataFrame using 'session.read.csv()' , perform Snowpark DataFrame transformations, and then write to the Snowflake table. Avoid using Pandas DataFrames altogether.
C) Read each CSV file into a Pandas DataFrame, perform transformations, and then create a Snowpark DataFrame from each Pandas DataFrame using Union all the Snowpark DataFrames.
D) Read each CSV file into a Pandas DataFrame, perform transformations, and then create a temporary table with the result of 'session.write_pandas' with auto create table=False' .
E) Read each CSV file into a Pandas DataFrame, perform transformations, and then create a temporary table with the result of 'session.write_pandas' with auto create table=True' .

4. You are developing a Snowpark application that requires calling a stored procedure. Which of the following approaches is the MOST secure and efficient way to call a stored procedure from your Snowpark Python code, assuming the stored procedure returns a single value?

A)

B)

C)

D)

E)

5. You are building a Snowpark Python application to perform complex data transformations and want to leverage external packages not pre-installed in the Snowflake environment. You need to ensure these packages are available within your Snowpark session. Which of the following methods are valid for deploying and using these third-party packages within your Snowpark Python environment? (Select TWO)

A) Manually install the packages on the Snowflake compute pool nodes before starting the Snowpark session.
B) Use the 'session.addDependency()' method to upload individual '.py' files containing the package code directly to the Snowflake internal stage.
C) Utilize Snowflake's Anaconda channel integration and specify the package names as strings in the method. Snowflake will automatically resolve and install the packages from the Anaconda channel.
D) Create a 'conda' environment file ('environment.yml') specifying the required packages and use the method to upload the environment definition. Snowflake will automatically install the packages within the session's environment.
E) Deploy the required packages using the SnowCLl package management commands, and then the Snowpark session will be able to automatically use the deployed packages.

Solutions: