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Your company is planning to implement a machine learning pipeline to support predictive analytics. Describe the steps you would take to design and implement an ETL pipeline for a machine learning project, and explain the considerations involved in preparing the data for machine learning models.
Your company is planning to implement a machine learning pipeline to support predictive analytics. Describe the steps you would take to design and implement an ETL pipeline for a machine learning project, and explain the considerations involved in preparing the data for machine learning models.
Explanation:
Option B is the correct answer. Designing a multi-stage ETL pipeline with data preprocessing and feature engineering steps allows for preparing the data for machine learning models effectively. Leveraging Apache Spark's machine learning libraries and frameworks can help in handling large volumes of data and performing complex transformations. Using a single-stage ETL process or a traditional statistical analysis approach may not provide the desired level of data preparation for machine learning models. Ignoring the data preparation aspect may lead to suboptimal model performance and accuracy.