Google Professional Machine Learning Engineer
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In the context of developing a machine learning model for a financial services company that must comply with strict regulatory requirements, including GDPR and financial reporting standards, the team is tasked with ensuring the model not only achieves high accuracy but also adheres to compliance and scalability constraints. The model will process sensitive customer data, and the company is under tight deadlines to deploy a compliant solution. Given these constraints, which of the following best describes the primary goal of hyperparameter tuning? Choose the best option from the options provided below.
Explanation:
Hyperparameter tuning is a critical step in the machine learning pipeline aimed at optimizing a model's performance by finding the best settings (hyperparameters) before the training process. This involves a systematic search over various hyperparameter combinations to enhance model accuracy, ensure it meets specific standards such as regulatory compliance in the financial sector, and consider computational efficiency to meet scalability constraints. Options A, C, and D describe other important aspects of the ML pipeline but do not accurately represent the primary goal of hyperparameter tuning, especially in a context that requires balancing performance, compliance, and scalability.