Generative AI Leader Responsible AI and Data Governance Practice Test 2 — 15 Questions

Question 1

A healthcare AI application provides diagnostic suggestions based on patient data. To comply with Google's AI Principles, which practice should be implemented?

Accepted Answer

Include a human-in-the-loop review for all diagnostic suggestions before finalizing. Google's AI Principles require accountability and safety, especially in high-stakes domains like healthcare. Human oversight ensures that AI suggestions are reviewed by qualified professionals before action is taken.

Answer

Use a simpler model that is less accurate but easier to explain

Answer

Limit the model's output to only the most common diagnoses to reduce risk

Answer

Deploy the model directly to production without human review to maximize speed

Question 2

A media company uses a generative AI model to create marketing images. They want to ensure that AI-generated images can be identified as synthetic. Which Google Cloud capability should they use?

Accepted Answer

SynthID. SynthID is Google DeepMind's technology for embedding invisible watermarks into AI-generated images, audio, text, and video, enabling identification of synthetic content.

Answer

Cloud Vision API

Answer

Data Loss Prevention (DLP) API

Answer

Vertex AI Model Registry

Question 3

A financial institution deploys a generative AI chatbot for customer inquiries. They must comply with the EU AI Act, which classifies their use as high-risk. What is a mandatory requirement under the Act?

Accepted Answer

Retain logs of system behavior and human oversight for traceability. The EU AI Act requires that high-risk AI systems maintain detailed technical documentation and logs for traceability and conformity assessment.

Answer

Register the AI system with the European Data Protection Board (EDPB)

Answer

Obtain a CE marking for the training dataset

Answer

Conduct an annual bias audit using an external auditor

Question 4

A research team wants to document the intended uses, limitations, and ethical considerations of their newly trained image classification model. Which Google Cloud tool should they use?

Accepted Answer

Model Card Toolkit. The Model Card Toolkit is specifically designed to document the intended uses, limitations, and ethical considerations of machine learning models, including image classification models. It generates a structured model card that provides transparency and accountability, which aligns directly with the team's goal of documenting these aspects.

Answer

Explainable AI SDK

Answer

Datasheets for Datasets

Answer

What-If Tool

Question 5

A global retailer uses a generative AI model to personalize product recommendations. They need to ensure that customer prompts and responses are not logged for model improvement to meet GDPR data minimization principles. Which configuration should they apply?

Accepted Answer

Disable prompt/response logging in Vertex AI endpoint settings. To comply with GDPR data minimization, the retailer should disable prompt/response logging. Vertex AI offers settings to control whether prompts and responses are stored for model improvement.

Answer

Use a third-party logging service with data deletion policies

Answer

Enable data logging for six months and then automatically delete

Answer

Anonymize all customer data before logging

Question 6

A social media platform wants to use generative AI to moderate harmful content. They need to detect hate speech, violence, and sexual content. Which Google Cloud service provides pre-built filters for these categories?

Accepted Answer

Natural Language API with content moderation. Google Cloud's Natural Language API includes content moderation models that can detect categories like hate speech, violence, and sexual content.

Answer

Dialogflow CX

Answer

Vertex AI Matching Engine

Answer

Cloud Vision API

Question 7

A startup develops a generative AI tool for legal document review. To ensure explainability, they want the model to cite specific clauses from source documents when making assertions. Which technique should they use?

Accepted Answer

Grounding using a retrieval system that provides source documents. Grounding in generative AI means the model cites verifiable sources for its outputs, which is essential for explainability in domains like law.

Answer

Fine-tuning on legal documents with citation examples

Answer

Chain-of-thought prompting

Answer

Prompt engineering to ask for citations

Question 8

A hospital deploys a generative AI model to assist in diagnosing rare diseases. The model sometimes suggests treatments that are not widely accepted. To align with Google's AI Principle of being accountable to people, what should the hospital implement?

Accepted Answer

Implement a human override mechanism that allows clinicians to reject AI suggestions. Option B is correct because Google's AI Principle of being accountable to people requires that AI systems remain under human control, especially in high-stakes domains like healthcare. A human override mechanism ensures that clinicians can reject AI suggestions that are not widely accepted, directly addressing the principle's emphasis on human oversight and responsibility for outcomes.

Answer

Provide confidence scores for each suggestion to help clinicians assess reliability

Answer

Limit the model to only suggest treatments that appear in a predefined list

Answer

Conduct a retrospective audit of all AI suggestions monthly

Question 9

A company develops a generative AI model for resume screening. They discover that the model is rejecting candidates from certain demographic groups disproportionately. Which step should they take first to address unfair bias?

Accepted Answer

Audit the training data for demographic representativeness. Before attempting to fix bias, it is essential to evaluate the training data for representativeness. Bias often originates from imbalanced or unrepresentative training data.

Answer

Reduce the model's complexity to avoid overfitting to biased patterns

Answer

Apply adversarial debiasing to the model

Answer

Collect more data from all demographic groups equally

Question 10

A multinational corporation uses Vertex AI to fine-tune a language model on proprietary customer data. They want to ensure that the fine-tuned model does not inadvertently memorize and regurgitate sensitive customer information. Which approach is most effective?

Accepted Answer

Apply differential privacy techniques during fine-tuning. Differential privacy during training limits memorization by adding noise, making it much harder for the model to leak sensitive data from the training set.

Answer

Regularly audit the model by prompting it with known sensitive phrases

Answer

Use a smaller model to reduce memorization capacity

Answer

Train the model with a higher learning rate to reduce overfitting

Question 11

A non-profit organization uses generative AI to produce reports on climate change. They want to ensure that the model's outputs are scientifically accurate. Which Google AI Principle is most relevant?

Accepted Answer

Uphold high standards of scientific excellence. The Google AI Principle 'Uphold high standards of scientific excellence' directly addresses the need for generative AI outputs to be scientifically accurate, especially in domains like climate change reporting where factual precision is critical. This principle emphasizes rigorous validation, peer review, and adherence to established scientific methodologies to ensure the model's outputs are reliable and trustworthy.

Answer

Be built and tested for safety

Answer

Be accountable to people

Answer

Be socially beneficial

Question 12

A software company wants to provide users with a clear understanding of when and why their AI system may produce incorrect answers. Which tool from the Responsible AI toolkit should they use to communicate model limitations?

Accepted Answer

Model Cards. Model Cards are designed to communicate model performance, intended use, and limitations to stakeholders in a standardized format.

Answer

People + AI Guidebook

Answer

PAIR Explorables

Answer

Datasheets for Datasets

Question 13

A fintech company is deploying a generative AI system that offers investment advice. To comply with regulations and Google's AI Principles, they need to ensure appropriate human oversight and transparency. Which two actions should they take? (Choose two.)

Accepted Answer

Require a human advisor to review and approve all AI-generated recommendations. Human review for high-stakes decisions (C) ensures accountability, and providing model cards (E) ensures transparency about capabilities and limitations.

Answer

Use only synthetic data for training to avoid privacy issues

Answer

Allow the AI to execute trades automatically without human intervention

Answer

Disable logging of all interactions to maximize user privacy

Question 14

A government agency is procuring a generative AI solution for public service information. They require that the system can provide explanations for its answers, protect citizen privacy, and comply with data residency laws. Which three capabilities should they mandate? (Choose three.)

Accepted Answer

Support for grounding with citations to source documents. Grounding (A) provides explainability through citations, data residency controls (C) ensure compliance with data location laws, and differential privacy (E) protects individual privacy during model training.

Answer

Integration with social media APIs

Answer

Ability to generate images from text descriptions

Question 15

A tech company wants to ensure that their generative AI model does not produce harmful content. They plan to use Google Cloud's content safety features. Which two methods can they use to customize content safety? (Choose two.)

Accepted Answer

Adjust safety thresholds for different categories like hate speech and violence. Vertex AI allows setting safety thresholds per category (B) and defining blocklists for specific terms (D) to customize content filtering.

Answer

Use the default safety filters without any modifications

Answer

Disable all safety filters for maximum model creativity

Answer

Train a separate model to detect harmful content