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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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 single session object with connection pooling enabled and let Snowpark handle warehouse switching automatically based on the queries executed.
C) 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.
D) 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.
E) Create separate sessions for each warehouse, but only close the last session created to avoid disconnecting all connections.

2. A data engineer is developing a Snowpark application using Python and needs to connect to Snowflake. They want to avoid hardcoding credentials directly in the script and utilize environment variables for authentication. Which of the following approaches is the MOST secure and RECOMMENDED way to retrieve Snowflake connection parameters (account, user, password, database, schema, warehouse, role) from environment variables and establish a Snowpark session?

A) Manually retrieve each parameter using 'os.environ.get()' and pass them directly into the 'Session.builder.configs()' method.
B) Utilize the 'Session.builder.getorcreate()' method, assuming that Snowflake connection information (user, password, account, warehouse, etc.) are already set as environment variables with standard names, and let Snowpark automatically infer the parameters.
C) Use the SnowCLl to configure a connection profile, and then reference this profile name when creating the Snowpark session. Ensure each environment variable is also separately available, but are not explicitly called in the code to establish connection but only to set up SnowCLl.
D) Store all connection parameters as a JSON string in a single environment variable and parse it within the Snowpark application.
E) Leverage the 'snowflake.connector.connect()' function with 'os.environ.get()' for credentials and then create a Snowpark session from the connection using Session. builder. from_connection(connectiony.

3. Consider the following Snowpark Python code snippet for creating a stored procedure:

What is the PRIMARY reason for explicitly defining 'input_types' and during the stored procedure registration?

A) To allow Snowflake to automatically generate documentation for the stored procedure's input and output types.
B) To allow Snowsight to correctly display the stored procedure's metadata, making it easier for users to understand its functionality.
C) To enable the stored procedure to be called from other programming languages besides Python.
D) To improve the performance of the stored procedure by enabling compile-time optimizations.
E) To ensure data type safety and schema validation during deployment and execution, preventing unexpected runtime errors due to type mismatches between the stored procedure and the calling environment.

4. A data engineering team is using Snowpark Python to build a complex ETL pipeline. They notice that certain transformations are not being executed despite being defined in the code. Which of the following are potential reasons why transformations in Snowpark might not be executed immediately, reflecting the principle of lazy evaluation? Select TWO correct answers.

A) Snowpark operations are only executed when an action (e.g., 'collect()', 'show()', is called on the DataFrame or when the DataFrame is materialized.
B) Snowpark automatically executes all transformations as soon as they are defined, regardless of whether the results are needed.
C) The size of the data being processed exceeds Snowflake's memory limits, causing transformations to be skipped.
D) Snowpark employs lazy evaluation to optimize query execution by delaying the execution of transformations until the results are actually required.
E) The 'eager_execution' session parameter is set to 'True'.

5. Consider two Snowpark DataFrames, 'employees' and 'departments' , with the following schemas: 'employees': (employee_id: Integer Type, employee_name: StringType, department_id: Integer Type, salary: IntegerType) 'departments': (department_id: Integer Type, department_name: StringType, location: StringType) You want to find the highest salary within each department, along with the department name and location, and display the results in a Snowpark DataFrame. Which of the following Snowpark Python code snippets correctly achieves this?

A)

B)

C)

D)

E)

Solutions: