Google Professional Machine Learning Engineer
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In a scenario where a large and continuously growing team of data scientists is using Google Cloud's AI Platform for developing and deploying machine learning models, the management of jobs, models, and versions becomes critical. The team is distributed across different regions and works on various projects simultaneously. Given the need for scalability, cost-effectiveness, and ease of management, which of the following strategies is the BEST to categorize, monitor, and manage these resources effectively? Choose the most appropriate option.
A
Implement strict IAM permissions on AI Platform notebooks to restrict access to a single user or group per instance, ensuring minimal overlap and maximum security.
B
Create a BigQuery sink for Cloud Logging logs, specifically filtering to capture AI Platform resource usage data, and develop a SQL view in BigQuery to map users to their respective resources for tracking and accountability.
C
Organize the work of each data scientist into separate projects, making their jobs, models, and versions accessible only to them, thereby isolating resources and simplifying management.
D
Utilize labels to descriptively categorize resources, enabling users to filter and monitor resources based on these labels, thus facilitating scalable and efficient resource management.
E
Combine the use of labels for resource categorization with the implementation of a BigQuery sink for Cloud Logging logs to enhance monitoring and accountability across the team. (Choose two options)