As a Machine Learning Engineer at a regulated insurance company, you are tasked with developing a model to approve or reject insurance applications. The model must not only be effective in its predictions but also comply with strict regulatory standards. Considering the need for auditability, consistency, and transparency, which of the following sets of factors are MOST critical to ensure the model meets both performance and compliance requirements? Choose the two most appropriate options.

Real Exam

Federated learning and differential privacy, to enhance data privacy across distributed datasets.

8.0%

Traceability and reproducibility, to ensure the model's development process is auditable and its results are consistent over time.

27.3%

Interpretability and redaction, to make the model's decisions understandable to stakeholders and protect sensitive information.

9.1%

Reproducibility and interpretability, to guarantee consistent model performance and clear understanding of its decisions.

14.8%

Traceability, reproducibility, and interpretability, to cover all aspects of auditability, consistency, and transparency.

40.9%

Google Professional Machine Learning Engineer