Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A large enterprise is deploying a generative AI-powered code assistant for their developers. The solution uses Vertex AI with a fine-tuned Codey model. The security team requires that all prompts and responses be logged for audit purposes, but the logs must not contain sensitive information such as API keys or passwords. The operations team is concerned about high latency during peak usage. You need to design a solution that meets security requirements without compromising performance. Which approach should you take?
⚠ Common exam trap
Google Cloud often tests the misconception that post-processing redaction (e.g., Cloud DLP) or custom proxies are acceptable for real-time logging, when in fact native streaming redaction via log sinks is required to meet both security and performance constraints.
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
✓
Enable Vertex AI model monitoring with Cloud Logging, and configure a log sink with a custom exclusion filter to redact sensitive patterns before storing
It uses Vertex AI model monitoring with Cloud Logging to capture prompts and responses, then applies a custom exclusion filter with a log sink to redact sensitive patterns (e.g., API keys, passwords) in real time before logs are stored. This meets the security requirement for audit logging without sensitive data while avoiding the latency overhead of post-processing or a custom proxy, thus satisfying the operations team's performance concern.
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 Cloud Audit Logs to capture all API calls to Vertex AI, but do not log the actual prompts and responses
Why it's wrong here
Audit Logs do not capture model input/output, failing the audit requirement.
- ✓
Enable Vertex AI model monitoring with Cloud Logging, and configure a log sink with a custom exclusion filter to redact sensitive patterns before storing
Why this is correct
This ensures all interactions are logged but sensitive data is removed, meeting security without major performance impact.
- ✗
Log all prompts and responses to Cloud Storage and use a Cloud DLP job to scan and redact sensitive data periodically
Why it's wrong here
Periodic redaction means sensitive data remains in logs temporarily, violating security requirements.
- ✗
Implement a custom proxy that logs all requests after stripping sensitive data, then forward to the model
Why it's wrong here
A proxy adds latency and complexity; not ideal for performance.
Go deeper
Related to this question
About these practice questions
Courseiva writes every Generative AI Leader question from scratch — 683 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.