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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are tasked with building a data pipeline that uses Snowpark to process customer data in a table called 'CUSTOMERS'. The table contains sensitive information, and you need to ensure that any Personally Identifiable Information (PII) is removed from the table after 30 days. You decide to implement a mechanism to automatically delete records older than 30 days. Which of the following approaches would you consider when designing the deletion process with consideration of cost, performance and security?

A) Create a Snowpark Stored Procedure that executes the 'DataFrame.delete()' operation and schedule this stored procedure to run daily using a Snowflake Task.
B) Implement a dynamic data masking policy on the PII columns instead of deleting records. Set the policy to mask data older than 30 days.
C) Implement a Snowflake Task that executes a SQL 'DELETE statement directly against the 'CUSTOMERS' table, filtering records based on the 'created_at' column.
D) Implement a Snowflake Stream on the 'CUSTOMERS' table. Create another table 'CUSTOMERS ARCHIVE' with the same schema and stream data older than 30 days using 'DataFrame.copy_into_table', then truncate data from original CUSTOMERS table
E) Schedule a daily Snowpark script to read the entire 'CUSTOMERS' table, filter out records older than 30 days based on the 'created_at' column, and then use DataFrame.delete()' to remove those records from the table.

2. A Snowpark application needs to authenticate to Snowflake using OAuth. The application is running on an Azure Function and uses a client ID, client secret, and refresh token obtained previously. Which of the following connection parameter dictionaries is correctly configured for OAuth authentication?

A)

B)

C)

D)

E)

3. You are developing a Snowpark application that involves creating a set of stored procedures and UDFs to process data'. To ensure proper version control and dependency management, you decide to package your Python code into a single Python Wheel file and deploy it to Snowflake. Which of the following methods are valid for deploying and utilizing this Python Wheel file within Snowflake, considering best practices for maintainability and security? (Select TWO)

A) Upload the Python Wheel file to an internal stage and directly reference it in the UDF or stored procedure definition using the 'USING' clause.
B) Use the Snowsight UI to upload the Python Wheel file as a dependency for the Snowflake environment, making it available for all stored procedures and UDFs within that environment.
C) Create a Conda environment file (environment.yml) that specifies the Python Wheel file as a dependency and use this file to create a Snowflake environment. Then, associate the stored procedures and UDFs with that environment.
D) Use the 'snowflake-cli' to push the Python Wheel file as a package, then add the package name to the list of packages when creating the stored procedure or UDF.
E) Upload the Python Wheel file to an external stage (e.g., AWS S3) and configure Snowflake to access the external stage, then reference the wheel file path in the USING' clause of the UDF or stored procedure.

4. You're working with Snowpark and want to load data from a Pandas DataFrame into a Snowpark DataFrame. The Pandas DataFrame, 'customer_data' , contains columns with mixed data types (integers, strings, dates). Some columns also contain NULL values. You need to ensure that the data types are correctly inferred by Snowpark, NULL values are handled appropriately, and the resulting Snowpark DataFrame 'snowpark_customers' can be used for further transformations. What is the best approach to achieve this with minimal code and maximum performance?

A) Use 'session.write_pandas' because its optimized for large pandas dataframe.
B) First, replace all NA/NaN values in Pandas DataFrame with None, then create Snowpark DataFrame using 'session.createDataFrame(customer_datay.
C) Use 'session.createDataFrame(customer_datay and rely on Snowpark to automatically infer the schema and handle NULL values implicitly. Convert any problematic columns after the Snowpark DataFrame is created.
D) Explicitly define the schema with StructType and StructField, specifying the column names and data types based on the Pandas DataFrame, converting null values to Snowflake's null representation during DataFrame creation.
E) Infer the schema explicitly before creating the Snowpark DataFrame using Pandas DataFrame column types. For the string columns, define them to be StringType().

5. You have a Python function, 'calculate metrics(df: snowpark.DataFrame, metric name: str) -> snowpark.DataFrame', that calculates various metrics on a Snowpark DataFrame. You want to deploy this function as a stored procedure in Snowflake. You need to ensure that the stored procedure has appropriate permissions to read data from a table named 'customer data' and write results to a table named 'metrics_table'. Which of the following steps are necessary to achieve this, assuming you are using the 'session.sproc.register' method?

A) When registering the stored procedure using 'session.sproc.register' , specify the argument and provide a 'replace=True' if necessary. This will allow you to assign ownership of the stored procedure to a role with the necessary privileges.
B) Grant the 'SELECT privilege on the 'customer_data' table and the 'INSERT privilege on the 'metrics_table' table to the role executing the stored procedure.
C) Grant the 'USAGE privilege on the database and schema containing the 'customer_data' and 'metrics_table' tables to the role executing the stored procedure.
D) Specify the 'imports' argument in 'session.sproc.register' with the list of packages which are needed to run 'calculate_metrics' function.
E) Specify the 'packages' argument in 'session.sproc.register' to include any Python dependencies required by the 'calculate_metrics' function.

Solutions: