Passing exam with SPS-C01 latest training materials

You are developing a Snowpark application that requires secure access to Snowflake. You need to establish a session using Key Pair authentication. You have stored your private key in an encrypted file and have the passphrase available. Assume you've correctly configured your Snowflake user with the public key. Which of the following methods can be used to load the private key, assuming that 'PRIVATE KEY PATH' stores path to encrypted private key and 'PRIVATE KEY PASSPHRASE stores passphrase?

• A.
  
• B.
  
• C.
  
• D.
  
• E.
  

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. Specify the 'imports' argument in 'session.sproc.register' with the list of packages which are needed to run 'calculate_metrics' function.
• B. Grant the 'USAGE privilege on the database and schema containing the 'customer_data' and 'metrics_table' tables to the role executing the stored procedure.
• C. 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.
• D. Specify the 'packages' argument in 'session.sproc.register' to include any Python dependencies required by the 'calculate_metrics' function.
• E. 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.

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.
  

You are developing a Snowpark application to load data into a Snowflake table named 'SALES DATA. The DataFrame 'sales_df contains new sales records. You need to insert these records into 'SALES DATA. Which of the following Snowpark DataFrame methods will efficiently perform this operation, considering potential data type mismatches between the DataFrame and the target table? Assume no explicit schema definition is necessary.

• A. Option E
• B. Option B
• C. Option C
• D. Option A
• E. Option D

Consider the following Snowflake SQL statement intended to modify the properties of a Snowpark-optimized virtual warehouse named
'SNOWPARK_WH':

Which of the following statements accurately describe the expected outcome of executing this SQL statement?

• A. The SQL statement will fail because you cannot modify 'WAREHOUSE_SIZE and 'MAX CLUSTER COUNT in a single SALTER WAREHOUSE statement.
• B. The SQL statement will fail because the 'SCALING POLICY parameter cannot be set for Snowpark-optimized warehouses.
• C. The SQL statement will execute successfully, resizing the 'SNOWPARK WI-i' warehouse to 'LARGE', setting the maximum number of clusters to 3, the minimum to 1, and enabling the 'ECONOMY' scaling policy.
• D. The SQL statement will execute successfully only if the user executing it has the 'MODIFY privilege on the 'SNOWPARK_WH' warehouse.
• E. The SQL statement will execute successfully after checking if 'SNOWPARK_WH' is of Snowpark-optimized warehouse type, resizing the 'SNOWPARK_WH' warehouse to 'LARGE', setting the maximum number of clusters to 3, the minimum to 1, and enabling the 'ECONOMY' scaling policy.