Google Professional Machine Learning Engineer
Get started today
Ultimate access to all questions.
As a Machine Learning Engineer at a leading financial institution, you are tasked with designing a secure and efficient biometric authentication system for the mobile banking app. The system must authenticate users via fingerprints while ensuring that sensitive biometric data is not stored in the bank's databases to comply with strict privacy regulations. Additionally, the solution must be scalable to accommodate millions of users worldwide and cost-effective to implement. Given these constraints, which machine learning strategy would you recommend for training and deploying this model effectively? Please choose the best option.
As a Machine Learning Engineer at a leading financial institution, you are tasked with designing a secure and efficient biometric authentication system for the mobile banking app. The system must authenticate users via fingerprints while ensuring that sensitive biometric data is not stored in the bank's databases to comply with strict privacy regulations. Additionally, the solution must be scalable to accommodate millions of users worldwide and cost-effective to implement. Given these constraints, which machine learning strategy would you recommend for training and deploying this model effectively? Please choose the best option.
Explanation:
Federated Learning is the most suitable strategy for this scenario because it enables the model to learn from data distributed across multiple devices without the need to centralize sensitive biometric information. This approach not only enhances user privacy and security by keeping personal data on the user's device but also aligns with the scalability and cost-effectiveness requirements by reducing the need for extensive data storage and processing infrastructure.