Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A bank is deploying a Gemini model on Vertex AI to draft responses to customer complaints. Compliance requires that the deployment (Choose two.)
⚠ Common exam trap
The trap here is treating transparency as equivalent to publishing model weights; in regulated deployments, transparency means documented controls, logging, and oversight rather than exposing the model itself.
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
✓
Records prompts and responses for audit purposes
A compliant generative AI deployment in a regulated setting needs traceability and output control. Logging prompts and responses creates the audit trail examiners expect, while configurable safety filters prevent harmful or policy-violating content from reaching customers. Bulk unreviewed training data, disabled logging, and public weight release either increase risk or remove evidence, so they do not belong in this deployment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disables all logging to reduce storage costs
Why it's wrong here
Disabling logging removes the audit trail regulators expect and makes it impossible to investigate incidents or prove that responses were reviewed. Cost reduction does not justify eliminating evidence, and doing so would directly conflict with the compliance objective, so this choice is the opposite of what a regulated deployment needs.
- ✓
Records prompts and responses for audit purposes
Why this is correct
Auditability is a core compliance requirement for regulated industries. Vertex AI provides logging and audit capabilities so that every prompt and generated response can be retained and reviewed, which lets the bank demonstrate what the model was asked and what it produced. Without this trace, the bank cannot investigate incidents or satisfy examiner requests for evidence of controlled use.
- ✗
Trains the model on the bank's entire historical email archive without review
Why it's wrong here
Indiscriminately training on historical emails risks ingesting sensitive personal data, outdated policies, and biased language, creating privacy and fairness exposure. Compliance programs require data minimization and lawful basis for processing, so bulk unreviewed training data would increase risk rather than satisfy the requirement, and it is not a control the deployment should include.
- ✗
Publishes the model's weights publicly to demonstrate transparency
Why it's wrong here
Publishing model weights does not improve regulatory compliance for customer complaint handling and could expose proprietary or fine-tuned information. Transparency for a bank is achieved through documentation, logging, and human oversight, not by releasing weights, so this action is irrelevant to the stated compliance requirement and introduces unnecessary risk.
- ✓
Applies content filters and safety settings to block harmful or non-compliant output
Why this is correct
Vertex AI offers configurable safety filters and responsible AI controls that screen model input and output. For a bank drafting customer complaint responses, these filters help prevent toxic, unsafe, or policy-violating content from reaching customers, which is a direct compliance control and a key part of a governed generative AI deployment.
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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.