How to Securely Access Internal Data for Vertex AI Training?

You work for a bank with strict data governance requirements. You recently implemented a custom model to detect fraudulent transactions. You want your training code to download internal data by using an API endpoint hosted in your project’s network. You need the data to be accessed in the most secure way, while mitigating the risk of data exfiltration. What should you do?

Enable VPC Service Controls for peerings, and add Vertex AI to a service perimeter. Source Reference Answer
Create a Cloud Run endpoint as a proxy to the data. Use Identity and Access Management (IAM) authentication to secure access to the endpoint from the training job.
Configure VPC Peering with Vertex AI, and specify the network of the training job.
Download the data to a Cloud Storage bucket before calling the training job.

Community Votes

65%

35%

65% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.

Community Insight

The exam tests knowledge of Google Cloud's native data loss prevention tools, with the common trap being the assumption that standard networking or IAM proxies provide equivalent exfiltration protection.

This question tests securing Vertex AI training jobs against data exfiltration using Google Cloud’s network security features. The community consensus strongly favors VPC Service Controls as the definitive solution for enforcing strict data boundaries and preventing unauthorized data transfer.

Option B is the most common wrong answer; candidates often choose a Cloud Run proxy with IAM because it seems architecturally clean, but it lacks the deep network-level enforcement and audit controls required to truly prevent data exfiltration at scale.

Community Discussion (12 comments)

lunalongo 👍 1 Selected: A

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

tardigradum 👍 2 Selected: A

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.

dija123 👍 1 Selected: A

A is correct

peppenapo7 👍 4 Selected: A

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

fitri001 👍 2 Selected: B

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.

pinimichele01 👍 1 Selected: A

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

Sunny_M 👍 2

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

guilhermebutzke 👍 3 Selected: B

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.

guilhermebutzke 👍 3

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.

ddogg 👍 1 Selected: A

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.

daidai75 👍 1 Selected: A

I will go with A.

pikachu007 👍 1 Selected: B

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

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Expert Analysis

Why the Answer Is Correct

VPC Service Controls (VPC-SC) creates a secure boundary around sensitive resources by defining service perimeters that restrict how data flows between services and networks. By adding Vertex AI to the perimeter and enabling VPC-SC for peerings, you ensure that the training environment cannot exfiltrate data outside the defined security zone. This aligns directly with the bank's strict governance requirements and explicitly addresses the "data exfiltration" keyword in the prompt.

Why the Other Options Are Wrong

Option B relies on application-layer proxies and IAM, which can be bypassed if credentials are compromised and do not enforce network-level data flow restrictions. Option C only establishes connectivity via VPC peering without any inherent data access or egress controls. Option D introduces unnecessary data duplication and storage risks, failing to address real-time API access while still lacking built-in exfiltration safeguards.

Community Comment Notes

Multiple users highlight that VPC-SC is explicitly designed to "mitigate data exfiltration risks," matching the exam's exact phrasing [1][6]. Comment [4] correctly notes that VPC-SC restricts access to only necessary resources within the perimeter. Comment [7] emphasizes that this approach provides the strongest security posture by combining network isolation with fine-grained service boundaries.

Official Reference

Exam Strategy

Always map keywords like "data exfiltration" and "strict governance" to Google Cloud's dedicated security services rather than generic networking or IAM solutions. When Vertex AI interacts with internal data, prioritize VPC Service Controls to establish enforceable data boundaries before considering application proxies or manual data staging.

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