DSA-C03 Exam Dumps | You are tasked with deploying a time series forecasting model within Snowflake using Snowpark Python.

<< Prev Question Next Question >>

Question 96/143

You are tasked with deploying a time series forecasting model within Snowflake using Snowpark Python. The model requires significant pre-processing and feature engineering steps that are computationally intensive. These steps include calculating rolling statistics, handling missing values with imputation, and applying various transformations. You aim to optimize the execution time of these pre- processing steps within the Snowpark environment. Which of the following techniques can significantly improve the performance of your data preparation pipeline?

A. Force single-threaded execution by setting to avoid overhead associated with parallel processing.

B. Utilize Snowpark's vectorized UDFs and DataFrame operations to leverage Snowflake's distributed computing capabilities.

C. Convert the Snowpark DataFrame to a Pandas DataFrame using and perform all pre-processing operations using Pandas functions before loading the processed data back to Snowflake.

D. Ensure that all data used is small enough to fit within the memory of the client machine running the Snowpark Python script, thus removing the need for distributed computing.

E. Write the feature engineering logic directly in SQL and create a view. Use the Snowpark DataFrame API to query the view, avoiding Python code execution within Snowpark.

Question 96/143

LEAVE A REPLY

Download PDF File