Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A media company is building an internal tool that generates first-draft marketing copy from campaign briefs. Legal insists that the tool never reproduce copyrighted third-party text verbatim, and the content team wants a measurable way to compare draft quality across prompt revisions. Which two-part approach best addresses both needs?
⚠ Common exam trap
The trap here is believing that lower temperature or human review eliminates verbatim reproduction, when recitation is a distinct detection capability that must be explicitly enabled and measured separately.
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 safety filters and recitation checking, and use Vertex AI evaluation to score draft quality across prompt versions.
Recitation checking inspects generated output for passages that closely match training data and can block or flag them, which is the systematic safeguard legal requires. Vertex AI evaluation supplies repeatable quality scores, so prompt revisions can be compared with evidence rather than opinion. Together they cover both the compliance and the measurement objectives in one design.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Lower the model temperature to zero and require editors to sign off on every draft before it is used.
Why it's wrong here
A low temperature makes output more deterministic but does not prevent the model from producing memorized passages, so the legal concern persists. Editorial sign-off is a manual gate, not a measurable quality comparison, and it does not give the content team data on which prompt revision performs better.
- ✓
Enable Vertex AI safety filters and recitation checking, and use Vertex AI evaluation to score draft quality across prompt versions.
Why this is correct
Recitation checking detects when generated content closely matches training data and can flag or block it, directly addressing the verbatim-copying concern. Vertex AI evaluation provides repeatable metrics on draft quality, giving the content team an objective basis to compare prompt revisions instead of relying on subjective impressions.
- ✗
Add a keyword blocklist that strips any phrase appearing in a public style guide from generated drafts.
Why it's wrong here
A blocklist only removes phrases someone anticipated in advance, so it cannot catch the unbounded range of third-party text a model might reproduce. It also distorts drafts by deleting legitimate wording, and it offers no mechanism for measuring draft quality across prompt iterations.
- ✗
Deploy the largest available model with maximum output tokens and rely on human editors to catch any copied passages.
Why it's wrong here
Relying on manual review places the burden of detecting verbatim reproduction on editors and provides no systematic safeguard or measurement. Maximizing output length also increases the chance of drifting into memorized text, so this approach raises legal exposure rather than reducing it.
Go deeper
Related to this question
About these practice questions
Courseiva writes every Generative AI Leader question from scratch — 1,008 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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.