Exam Professional Machine Learning Engineer topic 1 question 181 discussion

A is the right answer because it provides the strongest security posture, which the question statement emphasizes.VPC Service Controls offer a more robust defense against data exfiltration Why the other options are wrong: *B) If the proxy is compromised, data is exposed. *C) Peering establishes network connectivity; lacks inherent data access control *D) Downloading to Cloud Storage introduces data at rest vulnerability

VPC Service Controls: This feature allows you to define network boundaries (service perimeters) and control the flow of data between services. By adding Vertex AI to a service perimeter, you can restrict its access to only the necessary resources, including the API endpoint.   With peerings you can enable secure communication between your VPC and the VPC where Vertex AI is running, ensuring data stays within your network boundary.

A is correct

It's literally written in the description of this service: avoid data exfiltration.

Security: Cloud Run offers a secure environment to run your proxy code. IAM authentication ensures only authorized training jobs have access to the data endpoint. Data Minimization: The proxy can potentially filter or transform data before sending it to the training code, reducing the amount of sensitive information exposed. Network Isolation: The proxy acts as an additional layer of isolation between the training code and the internal data source.

To mitigate data exfiltration risks, your organization might also want to ensure secure data exchange across organizational boundaries with fine-grained controls. As an administrator, you might want to ensure the following: Clients with privileged access don't also have access to partner resources. Clients with access to sensitive data can only read public data sets but not write to them

It should be A, VPC service controls can reduce data exfiltration risks. https://cloud.google.com/vpc-service-controls/docs/overview

My Answer B: Creating a Cloud Run endpoint as a proxy to the data allows you to control access to the internal data through an API endpoint. By using IAM authentication, you can enforce strict access controls, ensuring that only authorized entities (such as your training job) can access the data. This approach helps mitigate the risk of data exfiltration by providing a secure and controlled access point to the internal data. - Option A: may help control access within Google Cloud Platform services, but it does not directly address securing access to the internal data through an API endpoint. - Option C: is more about network configurations and does not provide a solution for securely accessing the internal data through an API endpoint. - Option D: transferring the data to a Cloud Storage bucket, which might introduce additional security risks during the data transfer process.

My Answer B: Creating a Cloud Run endpoint as a proxy to the data allows you to control access to the internal data through an API endpoint. By using Identity and Access Management (IAM) authentication, you can enforce strict access controls, ensuring that only authorized entities (such as your training job) can access the data. This approach helps mitigate the risk of data exfiltration by providing a secure and controlled access point to the internal data. - Option A: may help control access within Google Cloud Platform services, but it does not directly address securing access to the internal data through an API endpoint. - Option C: is more about network configurations and does not provide a solution for securely accessing the internal data through an API endpoint. - Option D: involves transferring the data to a Cloud Storage bucket, which might introduce additional security risks during the data transfer process.

A. https://cloud.google.com/security/vpc-service-controls?hl=en The first benefit on the official google cloud site is "Mitigate data exfiltration risks" Here's why: VPC Service Controls: This powerful tool allows you to restrict the network connectivity of resources within your VPC network. By enabling it for peerings, you can control which services within your project can access specific internal resources. Service perimeter: Adding Vertex AI to a service perimeter further restricts its access to only approved internal resources, including the API endpoint for your bank's data. This creates a secure zone where your model training can happen without jeopardizing sensitive data.

I will go with A.

It provides a controlled and secure way to allow the training job to access the necessary data while adhering to strict data governance requirements.