Google Professional Machine Learning Engineer
Comments
Loading comments...
Ultimate access to all questions.
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.
A
To expedite the model deployment process by randomly selecting hyperparameters, thereby meeting the regulatory deadlines without thorough validation.
B
To systematically search for the optimal combination of hyperparameters that maximizes the model's performance on validation data, ensuring it meets both accuracy and compliance standards, while also considering computational efficiency.
C
To preprocess the financial data to remove any sensitive information before model training begins, focusing solely on data privacy without addressing model performance.
D
To select the most relevant dataset from a pool of available financial datasets that complies with regulatory requirements, ignoring the model's hyperparameter configuration.