Snowflake SPS-C01 : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are working with a Snowpark DataFrame containing customer data'. One of the columns, 'phone number', contains phone numbers in various formats (e.g., '123-456-7890', '(123) 456-7890', '1234567890'). You need to standardize all phone numbers to the format '+1-123-456-7890' using Snowpark for Python. You also want to handle cases where the phone number is NULL gracefully, replacing them with '+1-000-000-0000'. Which of the following Snowpark code snippets is the most efficient and correct way to achieve this?

A)

B)

C)

D)

E)

2. Consider a Snowflake table 'sales_data' with a VARIANT column 'order_details' containing an array of JSON objects, where each object represents an item in an order. Each item object has fields like 'quantity', and 'price'. You need to calculate the total price for each order by summing the product of 'quantity' and 'price' for all items in the 'order_details' array. Which of the following Snowpark Python snippets correctly accomplishes this?

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, and then create a Snowpark DataFrame from each Pandas DataFrame using Union all the Snowpark DataFrames.
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 temporary table with the result of 'session.write_pandas' with auto create table=False' .
D) 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()'.
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 tasked with creating a secure UDF in Snowflake using Snowpark Python API that encrypts sensitive customer data before storing it. The UDF should be accessible only to users with specific roles. You have the following code snippet. What needs to be done to make it secure and operationalize for multiple users?

Considering Security best practices, what steps are critical for securing the UDF and granting appropriate permissions to other users?

A) Grant global USAGE privilege on the function's stage location to all users.
B) Use Snowflake's Secret object to securely store the encryption key and retrieve it within the UDF. Grant USAGE privilege on the secret to specific roles.
C) Grant EXECUTE privilege on the UDF and USAGE privilege on the database and schema containing the UDF to the roles needing access.
D) Store the encryption key directly in the UDF code for simplicity.
E) Bypass role-based access control and use shared credentials for UDF execution.

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) 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.
B) 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.
C) Manually install the packages on the Snowflake compute pool nodes before starting the Snowpark session.
D) Deploy the required packages using the SnowCLl package management commands, and then the Snowpark session will be able to automatically use the deployed packages.
E) Use the 'session.addDependency()' method to upload individual '.py' files containing the package code directly to the Snowflake internal stage.

Solutions: