Explanation:
Correct Answers: B and D. Developing a scalable machine learning model that predicts customer churn and integrates seamlessly with the company's existing infrastructure ensures that the solution is cost-effective and meets the business's scalability needs. Additionally, conducting A/B testing on different customer segments can provide valuable insights into the most effective retention strategies, complementing the machine learning approach. This combined strategy aligns with the Professional Machine Learning Engineer's goal of creating value for the business by addressing both the technical and business challenges effectively.
Incorrect Options:
Ultimate access to all questions.
No comments yet.
In the context of a Professional Machine Learning Engineer's role in 'Translating business challenges into ML use cases', consider the following scenario: A retail company wants to reduce customer churn by predicting which customers are most likely to stop using their services. The company has a large amount of historical customer data but is concerned about the cost and scalability of implementing a machine learning solution. Given these constraints, what is the BEST approach for the engineer to take to create value for the business through machine learning? Choose the two most appropriate options.
A
Focus solely on maximizing the accuracy of the predictive model without considering the implementation costs.
B
Develop a scalable machine learning model that predicts customer churn and integrates seamlessly with the company's existing infrastructure, ensuring cost-effectiveness.
C
Ignore the business's scalability concerns and proceed with building the most complex model possible to ensure high accuracy.
D
Conduct A/B testing on different customer segments to determine the most effective retention strategies without using machine learning.
E
Both B and D