mediumMultiple Choice
Generative AI Leader Practice Question: A healthcare startup is using a large language…
A healthcare startup is using a large language model (LLM) to generate discharge summaries. To comply with regulations, they need to ensure that a human reviews all AI-generated summaries before they are sent to patients. Which Google Cloud feature should they use to enforce this workflow?
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
✓
Vertex AI Human-in-the-Loop (HITL)
Human oversight is a key requirement for high-stakes AI systems. Vertex AI Human-in-the-Loop (HITL) provides a managed workflow to route predictions for human review, approval, or override before final output.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Audit Logs
Why it's wrong here
Cloud Audit Logs record who did what, but they cannot gate a workflow; nothing stops a summary being sent before review. It is tempting because audit trails are genuinely required for healthcare compliance evidence, and would be the right choice for retrospectively proving that reviews occurred.
- ✓
Vertex AI Human-in-the-Loop (HITL)
Why this is correct
Vertex AI Human-in-the-Loop enforces a mandatory review step before AI-generated discharge summaries reach patients, directly satisfying the regulatory constraint. It pauses the pipeline for clinician approval, so no summary is dispatched without human sign-off, unlike post-hoc logging or evaluation tools that cannot block delivery.
- ✗
Vertex AI Model Registry
Why it's wrong here
Vertex AI Model Registry versions and tracks models; it has no mechanism to block a generated summary from reaching a patient until a clinician approves it. It is tempting because it is the correct choice when the goal is governing model lineage, approval and rollout of model artefacts rather than individual outputs.
- ✗
Vertex AI Evaluation Service
Why it's wrong here
Vertex AI Evaluation Service scores model outputs against metrics offline; it cannot intercept a live discharge summary and hold it pending human sign-off. It is tempting because it is the natural fit when the requirement is measuring generative model quality before deployment, not enforcing a runtime approval gate.
Go deeper
Related to this question
About these practice questions
This Generative AI Leader question is part of Courseiva's 1,008-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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.