Snowflake SPS-C01 actual dump : Snowflake Certified SnowPro Specialty - Snowpark

You are optimizing a Snowpark Python application that performs complex data transformations on a large dataset. You notice significant performance bottlenecks. Which of the following optimization techniques would be MOST effective in leveraging the Snowpark architecture to improve performance?

• A. Converting all Snowpark DataFrames to Pandas DataFrames before performing any transformations.
• B. Manually partitioning the DataFrame into smaller chunks before applying transformations.
• C. Exploiting lazy evaluation by chaining transformations together and avoiding unnecessary 'collect()' or 'toPandas()' calls.
• D. Using User-Defined Functions (UDFs) written in Python for all transformations, ensuring they are vectorized where possible, instead of native Snowpark functions.
• E. Using 'session.sql()' whenever possible instead of Snowpark DataFrame operations.

You are working with a Snowpark DataFrame containing employee data, including columns 'employee_id', 'first_name', 'last_name', 'salary', and 'department'. You need to perform the following transformations: 1. Concatenate 'first_name' and into a new column called separating them with a space. 2. Increase each employee's salary by a percentage based on their 'department'. Department 'Sales' gets a 10% raise, 'Marketing' gets a 15% raise, and all other departments get a 5% raise. 3. Create a new column reflecting this raise. Which of the following Snowpark code snippets achieves these transformations correctly and efficiently? (Select all that apply)

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

A data scientist has developed a Snowpark Python stored procedure named 'model_training'. This procedure utilizes a large machine learning model and requires significant compute resources. The data scientist wants to optimize the cost and performance of running this stored procedure. Which of the following strategies would be the MOST effective for achieving this goal?

• A. Convert the Python stored procedure to a SQL stored procedure to leverage Snowflake's SQL optimization engine.
• B. Run the stored procedure on a larger Snowflake warehouse to reduce execution time, regardless of potential idle time.
• C. Specify a warehouse size using the 'warehouse' parameter within the '@sproc' decorator and leverage auto-suspend and auto-resume features to minimize costs when the procedure is idle.
• D. Register the stored procedure with the '@sproc' decorator without specifying any warehouse size, letting Snowflake automatically manage the warehouse.
• E. Split the stored procedure into multiple smaller procedures and execute them sequentially on a smaller warehouse.

You are working with a Snowpark DataFrame called 'customer df that contains customer data, including a column named 'registration_date' of data type TIMESTAMP NTZ. You need to filter the DataFrame to only include customers who registered in the year 2023. Which of the following Snowpark code snippets represents the MOST efficient and correct way to accomplish this filtering, considering potential timezone issues?

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

Consider the following Snowpark code snippet that aims to calculate the rank of each employee based on their salary within their respective department. What are potential issues with this code, and how can you improve it? (Select all that apply.)

• A. The code is correct and will produce the desired output without any issues.
• B. It is missing the 'col' function call in the orderBy clause. It should be 'orderBy(sf.col("salary").desc())'.
• C. The code does not handle potential null values in the salary column. Consider using or before calculating the rank.
• D. The 'rank()' function will produce dense ranks, which might be undesirable if there are ties in salary. Use for contiguous ranks instead.
• E. There may be performance issues if the employee table is very large. Consider adding a filter to the DataFrame before applying the window function.