- A
Use VPC Service Controls to create a perimeter around Vertex AI resources
VPC-SC prevents data from leaking outside the perimeter.
- B
Apply IAM roles with least privilege and use service accounts for the pipeline
Least privilege minimizes risk of unauthorized access.
- C
Expose the prediction endpoint publicly with an API key
Why wrong: Public endpoints increase attack surface; use private endpoints with IAM.
- D
Enable data encryption at rest using Cloud KMS
Encryption protects data at rest from unauthorized access.
- E
Disable audit logging to reduce data exposure
Why wrong: Audit logging is crucial for security monitoring and compliance.
Question 594 of 997
Securing a Generative AI Pipeline on Vertex AI with Sensitive Data
This Generative AI Leader practice question tests your understanding of google cloud's generative ai offerings. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE steps are required to secure a generative AI pipeline that uses Vertex AI and involves sensitive customer data?
Quick Answer
The answer is enabling data encryption at rest using Cloud KMS, implementing VPC Service Controls, and applying IAM with least privilege. These three steps form the foundation for securing a generative AI pipeline on Vertex AI with sensitive data because encryption protects stored data from unauthorized access, VPC Service Controls prevent data exfiltration by restricting network boundaries, and least-privilege IAM ensures only necessary permissions are granted to users and services. On the Google Cloud Generative AI Leader exam, this question tests your ability to distinguish secure configurations from common pitfalls—like using a public endpoint with an API key, which exposes the pipeline, or disabling audit logging, which removes visibility into threats. A frequent trap is assuming encryption alone is sufficient, but defense-in-depth requires all three controls working together. Memory tip: think “EVI” for Encryption, VPC, and IAM—the three pillars that lock down sensitive data from storage to access to network egress.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use VPC Service Controls to create a perimeter around Vertex AI resources
VPC Service Controls are required to create a service perimeter around Vertex AI resources, preventing data exfiltration by restricting data movement across the perimeter boundary. This is critical for sensitive customer data because it mitigates the risk of unauthorized access or leakage, even from within the same project or organization.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use VPC Service Controls to create a perimeter around Vertex AI resources
Why this is correct
VPC-SC prevents data from leaking outside the perimeter.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Apply IAM roles with least privilege and use service accounts for the pipeline
Why this is correct
Least privilege minimizes risk of unauthorized access.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Expose the prediction endpoint publicly with an API key
Why it's wrong here
Public endpoints increase attack surface; use private endpoints with IAM.
- ✓
Enable data encryption at rest using Cloud KMS
Why this is correct
Encryption protects data at rest from unauthorized access.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Disable audit logging to reduce data exposure
Why it's wrong here
Audit logging is crucial for security monitoring and compliance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse API key authentication (Option C) as a valid security measure, but for sensitive data, API keys lack identity binding and are considered a weak secret, whereas VPC Service Controls and IAM provide defense-in-depth.
Detailed technical explanation
How to think about this question
VPC Service Controls use context-aware access policies that evaluate attributes like identity, device, and IP address before allowing requests to Vertex AI endpoints. Under the hood, this is enforced via a service perimeter that blocks all egress traffic to unauthorized destinations, even if the caller has valid IAM permissions, effectively creating a data boundary that aligns with zero-trust principles.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Google Cloud's Generative AI Offerings — This question tests Google Cloud's Generative AI Offerings — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use VPC Service Controls to create a perimeter around Vertex AI resources — VPC Service Controls are required to create a service perimeter around Vertex AI resources, preventing data exfiltration by restricting data movement across the perimeter boundary. This is critical for sensitive customer data because it mitigates the risk of unauthorized access or leakage, even from within the same project or organization.
What should I do if I get this Generative AI Leader question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.
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