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As a Professional Machine Learning Engineer, you are tasked with defining how the model output should be utilized to address a specific business problem. The business aims to reduce customer churn by predicting which customers are likely to leave in the next month. The model's output will be used by the customer retention team to prioritize outreach efforts. Given the constraints of limited budget for customer outreach and the need for high precision in predictions to maximize ROI, which of the following is your primary responsibility? Choose the best option.
A
Designing a complex neural network architecture to ensure the highest possible accuracy, regardless of computational costs.
B
Collecting as much customer data as possible, including irrelevant features, to feed into the model.
C
Bridging the gap between the machine learning model's predictions and the business's objectives by ensuring the model's outputs are actionable and aligned with the retention team's capacity.
D
Focusing solely on data preprocessing to clean and normalize the data before model training.
E
Both A and C are correct because high accuracy and alignment with business objectives are equally important.