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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider a Snowflake table named 'raw events' containing web event data in a VARIANT column called 'event data'. Each 'event_data' JSON object may contain an array of product IDs under the key 'product_ids', and another array of user IDs who interacted with those products under the key 'user_ids'. These arrays are not guaranteed to be the same length, and a missing array should be handled gracefully (return null/empty result for that row rather than throwing error). Your objective is to create a Snowpark DataFrame that flattens both arrays into rows, pairing each product ID with each user ID for the specific event, generating as many rows as possible, and including event timestamp. You can use a zero-based index for the array elements using 'ARRAY CONSTRUCT()' and then using that array for using the EXPLODE function. Which of the following are necessary steps to fulfill this requirement and which are not?

A) Use 'iff()' or 'case when' and and other relevant function to handle exception cases.
B) Convert the 'product_ids' and 'user_ids' arrays to strings using and before applying 'explode'.
C) Use function to dynamically create an array of indices for the 'explode' function and use and function to handle missing values
D) Use two separate 'withColumn()' transformations followed by two 'explode()' operations to flatten each array independently.
E) Use a 'LEFT OUTER JOIN LATERAL FLATTEN' to flatten 'product_ids' and 'user_ids', and then use 'nvl()' or 'coalesce()' to handle cases where one or both arrays are missing.

2. You are building a Snowpark application to process sensitive data'. To enhance security, you want to leverage ephemeral sessions. Which configurations, passed to 'snowpark.Session.builder.configS , are required and sufficient to create an ephemeral session? Assume your Snowflake environment is properly configured to allow ephemeral sessions.

A) Setting the 'role' parameter to a role with appropriate privileges and 'warehouse'.
B) Ephemeral sessions do not have specific configurations; they are automatically created by Snowflake when the application connects.
C) Setting the 'role' parameter to a role with appropriate privileges and 'database' and 'schema'.
D) Setting the 'role' parameter to a role with appropriate privileges, 'database', 'schema', and 'warehouse'.
E) Only setting the 'role' parameter to a role with appropriate privileges.

3. You have a Pandas DataFrame 'products df containing product information that you want to upload to a Snowflake table named 'PRODUCTS' within the schema 'SALES'. The table might or might not exist. If it doesn't, you want to create it automatically. The products df DataFrame has a column 'product_name' that contains Unicode characters. Furthermore, you need to specify the column types directly as part of the operation. Which of the following options provides the most appropriate approach to achieve this with Snowpark?

A)

B)

C)

D)

E)

4. You are tasked with creating a Snowpark stored procedure that needs to access a secret stored in Snowflake's Secret Managen The secret contains credentials required to connect to an external API. Which of the following steps are necessary to correctly and securely access and use the secret within your Snowpark stored procedure? (Select all that apply)

A) Create a UDF that exposes the secret and call that UDF in the stored procedure.
B) Grant the USAGE privilege on the secret to the role that will execute the stored procedure.
C) Use the method within the stored procedure to retrieve the secret value.
D) Store the secret value directly in the stored procedure's code as a global variable.
E) Ensure that the stored procedure is created with the 'EXECUTE AS CALLER clause.

5. A Snowpark application needs to manage multiple sessions concurrently, each connected to a different Snowflake warehouse for resource isolation. The application receives warehouse names dynamically at runtime. How can the application efficiently create and manage these sessions while ensuring proper resource cleanup?

A) Create a single session and create multiple Snowpark DataFrames, specifying the warehouse in each DataFrame's 'createOrReplaceTempView' method.
B) Create a global session object and use 'session.use_warehouse(warehouse_name)' before each operation. This implicitly switches the warehouse for all operations on that session.
C) Create a separate session object for each warehouse using a loop and store them in a list. Ensure each session is closed using 'session.close()' in a 'finally' block after use or using a context manager.
D) Create separate sessions for each warehouse, but only close the last session created to avoid disconnecting all connections.
E) Create a single session object with connection pooling enabled and let Snowpark handle warehouse switching automatically based on the queries executed.

Solutions: