Generative AI Leader Responsible AI and Data Governance Practice Test 5 — 15 Questions

Question 1

A company wants to deploy a chatbot for customer support. To comply with the AI Principle of being 'accountable to people', which practice should they implement?

Accepted Answer

Provide human review for high-stakes decisions made by the chatbot. Providing human oversight for high-stakes decisions ensures accountability, aligning with Google's AI Principle that AI systems should be accountable to people.

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Allow the chatbot to escalate all conversations to human agents

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Ensure the chatbot can override human agents' decisions

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Disable logging of chatbot interactions to protect user privacy

Question 2

What is the primary purpose of SynthID, developed by Google DeepMind?

Accepted Answer

To embed an invisible watermark in AI-generated content for identification. SynthID is designed to embed invisible watermarks into AI-generated content to help identify its origin.

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To improve the factual accuracy of AI-generated text

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To automatically filter harmful content in AI outputs

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To reduce bias in training datasets

Question 3

A data scientist is building a text generation model and wants to evaluate bias using diverse test sets. Which Google AI Principle is directly relevant to this practice?

Accepted Answer

Avoid creating or reinforcing unfair bias. The principle of avoiding creating or reinforcing unfair bias directly calls for evaluating and mitigating bias, including using diverse test sets.

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Be built and tested for safety

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Incorporate privacy design principles

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Be socially beneficial

Question 4

A healthcare startup wants to use Vertex AI to deploy a model that helps doctors diagnose rare diseases. The model must be explainable, showing the reasoning path. Which technique should they implement?

Accepted Answer

Implement chain-of-thought prompting to output reasoning steps. Chain-of-thought reasoning allows the model to generate step-by-step explanations, which is crucial for medical diagnosis transparency.

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Use a black-box ensemble model for higher accuracy

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Reduce the model size to make it inherently interpretable

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Disable all safety filters to avoid interfering with model output

Question 5

A company is using a fine-tuned generative model to create marketing copy. They want to ensure that when the model references statistics, it provides citations to original sources. Which technique should they use?

Accepted Answer

Grounding by referencing source documents in the output. Option B is correct because grounding by referencing source documents in the output directly ties the generated statistics to verifiable original sources, ensuring citation accuracy. This technique retrieves and cites specific passages from trusted documents, which is essential for responsible AI in marketing copy where factual claims must be traceable.

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Model tuning with citation data

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Chain-of-thought prompting

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Confidence indicators

Question 6

A financial institution wants to ensure compliance with GDPR when using a generative AI service that processes EU user data. Which measure is most directly required?

Accepted Answer

Implement a mechanism to obtain user consent before processing data. GDPR requires explicit consent for processing personal data, making consent mechanisms a core requirement.

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Disable logging of all interactions to minimize data retention

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Store all prompts and responses in a US-based data center

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Use a model trained only on non-EU data

Question 7

A social media company uses a generative AI to moderate user comments. They need to filter hate speech, violence, and sexual content. What is the most efficient way to implement content safety in Vertex AI?

Accepted Answer

Use Google's pre-built safety filters provided with Vertex AI. Option D is correct because Google's pre-built safety filters in Vertex AI are specifically designed for content moderation tasks like hate speech, violence, and sexual content detection. They are immediately available, require no custom training, and integrate directly with Vertex AI's generative AI workflows, making them the most efficient choice for a social media company needing rapid deployment.

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Hire human moderators to manually review all comments

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Use a third-party API for content moderation

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Train a custom content classifier from scratch using Vertex AI AutoML

Question 8

A company is developing a generative AI application that will be used by customers in multiple countries, including those with strict data residency laws. How should they approach data governance?

Accepted Answer

Use data residency controls to keep data in specified regions. Option C is correct because data residency controls, such as those provided by cloud providers (e.g., AWS Organizations SCPs, Azure Policy, or GCP Organization Policies), allow the company to enforce that data is stored and processed only within specified geographic regions. This directly addresses strict data residency laws by preventing data from leaving the jurisdiction, which is a fundamental requirement for compliance with regulations like GDPR or Brazil's LGPD. Unlike workarounds, this approach provides native, auditable enforcement at the infrastructure level.

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Store all data in a single central data center to simplify management

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Use a VPN to route data through compliant regions

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Anonymize all data before processing to avoid residency issues

Question 9

A company plans to commercially use images generated by a text-to-image model. What should they check to avoid copyright issues?

Accepted Answer

The training data provenance and the model's license terms. Option A is correct because copyright issues arise from the training data and the model's license. The training data provenance determines if the model was trained on copyrighted works without permission, and the license terms specify whether commercial use is allowed. Without checking both, the company risks infringing on original creators' rights.

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The model's accuracy on standard benchmarks

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The model's latency and throughput

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The model's bias evaluation results

Question 10

A large enterprise is deploying a generative AI system for automated contract review. The system must provide confidence indicators for its legal analysis. How should confidence indicators be implemented to maximize transparency?

Accepted Answer

Provide a numerical confidence score between 0 and 1 for each conclusion. Option B is correct because providing a numerical confidence score between 0 and 1 for each conclusion directly quantifies the model's certainty, enabling users to assess the reliability of each legal analysis. This approach maximizes transparency by allowing legal professionals to calibrate their trust in the AI's output, which is critical for high-stakes contract review where false positives or negatives carry significant risk.

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Show the top-k most likely outcomes without probabilities

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Display a binary pass/fail indicator for each analysis

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Hide confidence indicators to avoid confusing users

Question 11

A company is using Vertex AI to fine-tune a language model on customer data. To comply with Google's privacy design principles, what should they do regarding the data used for fine-tuning?

Accepted Answer

Obtain explicit consent from users whose data is used for fine-tuning. Option B is correct because Google's privacy design principles require that user data used for fine-tuning must be obtained with explicit consent, ensuring transparency and user control. This aligns with the 'User Control and Transparency' principle, which mandates that users should be informed and give consent before their data is used to train or fine-tune models like those on Vertex AI.

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Use data augmentation to create synthetic data and discard original data

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Anonymize the data and publish it for public research

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Retain all fine-tuning data indefinitely for model improvement

Question 12

An organization uses a generative AI model to automatically approve or reject loan applications. To comply with the EU AI Act's requirements for high-risk AI systems, what must they implement?

Accepted Answer

A human-in-the-loop review for all loan decisions. The EU AI Act mandates that high-risk AI systems, such as those used for credit scoring and loan approvals, must include human oversight to mitigate risks of automated bias and errors. A human-in-the-loop (HITL) review ensures that each loan decision is subject to human judgment, allowing for intervention in edge cases or when the model's confidence is low. This directly satisfies the Act's requirement for meaningful human control over high-risk AI outputs.

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Publish the model's accuracy metrics on a public website

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A fully automated decision process with no human involvement

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Regular bias audits without human review of individual decisions

Question 13

A machine learning engineer is using a generative AI model that occasionally outputs biased content. Which TWO actions should they take to address this issue?

Accepted Answer

Adjust the training data to be more representative of the target population. Evaluating bias with diverse test sets helps identify issues, and adjusting training data representativeness helps mitigate bias at the source.

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Use a single test set for simplicity

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Remove all safety filters to allow more freedom in outputs

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Increase the model's size to improve generalization

Question 14

A company wants to deploy a generative AI application that must comply with both Google's AI Principles and the EU AI Act. Which THREE practices should they adopt?

Accepted Answer

Use data minimization and purpose limitation to respect privacy. Option B is correct because data minimization and purpose limitation are core requirements under both the EU AI Act (Article 10 on data governance) and Google's AI Principles (Privacy and Security). In generative AI, this means training and inference should use only the minimum necessary data, with clear constraints on how data is processed, to reduce privacy risks and comply with regulatory mandates.

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Use the largest possible model for best accuracy

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Disable logging to minimize data retention

Question 15

A data scientist is documenting a new dataset for a generative AI project. According to the Responsible AI toolkit, which TWO elements should they include in a Datasheet for Datasets?

Accepted Answer

The demographic composition of the data subjects. Option B is correct because the Responsible AI toolkit's Datasheet for Datasets framework requires documenting the demographic composition of data subjects to identify potential biases and ensure fairness. This transparency allows downstream users to assess whether the dataset may lead to discriminatory outcomes in generative AI models, aligning with responsible AI principles.

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The model architecture used to collect the data

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The hyperparameters of the model that will process the data

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The cost of acquiring the dataset