Generative AI Leader Responsible AI and Data Governance Practice Test 7 — 15 Questions

Question 1

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to retrieve relevant policy document chunks from a vector store at inference time, eliminating the need to retrain the model when documents are updated monthly. This keeps the system cost-effective and scalable, as only the vector index needs to be refreshed, not the underlying LLM.

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Answer

Fine-tune a base LLM on the policy documents monthly

Answer

Train a custom model from scratch on the policy documents each month

Question 2

According to Google's AI Principles, which of the following is a key commitment regarding privacy?

Accepted Answer

AI systems should incorporate privacy design principles, including notice and consent. Option A is correct because Google's AI Principles explicitly commit to incorporating privacy design principles, such as notice and consent, into AI systems. This means that AI systems should be designed with privacy safeguards from the outset, ensuring users are informed about data collection and have control over their data, aligning with frameworks like GDPR and privacy-by-design.

Answer

AI systems should share all data with third parties for transparency

Answer

AI systems should collect as much data as possible to improve accuracy

Answer

AI systems should store data indefinitely for future analysis

Question 3

A healthcare organization deploys a generative AI model to assist in diagnosing rare diseases from medical images. To comply with the EU AI Act's requirements for high-risk AI systems, what is the MOST critical control they must implement?

Accepted Answer

Implement a human-in-the-loop review process with the ability to override AI recommendations. The EU AI Act requires human oversight for high-risk AI systems, including the ability for humans to override or stop the system's decisions.

Answer

Use a model with a high F1 score to minimize the need for human review

Answer

Ensure the model is trained on synthetic data only to avoid privacy concerns

Answer

Deploy the model in a sandbox environment without real patient data

Question 4

A data scientist observes that their text generation model frequently uses gender stereotypes when generating job descriptions. Which responsible AI practice should be applied FIRST to address this issue?

Accepted Answer

Evaluate the model's outputs for bias using a diverse test set that includes gender-neutral prompts. Option D is correct because the first step in addressing model bias is to evaluate and measure the bias using a diverse, representative test set. This diagnostic step identifies the specific types and severity of gender stereotypes before any mitigation technique is applied, ensuring that subsequent interventions are targeted and effective. Without this evaluation, any corrective action risks being misapplied or introducing new biases.

Answer

Fine-tune the model on a dataset with more male-dominated job descriptions

Answer

Apply SynthID watermarking to the generated content

Answer

Increase the number of safety filters to block all gender-related content

Question 5

Which Google Cloud tool allows you to document the intended use, training data, and evaluation results of a machine learning model to promote transparency?

Accepted Answer

Model Cards. Model Cards are designed to provide transparent documentation of ML models, including intended use, training data, and evaluation results.

Answer

PAIR Explorables

Answer

Datasheets for Datasets

Answer

People + AI Guidebook

Question 6

A financial services company uses a generative AI model to produce customer-facing investment advice. They need to ensure the model's outputs can be traced back to specific sources. Which explainability technique is BEST suited for this requirement?

Accepted Answer

Grounding (citing sources). Grounding (citing sources) is the best technique because it directly links each generated output to specific, verifiable source documents or data points. This ensures traceability, which is critical for regulated financial advice where every claim must be attributable to a known reference, such as a regulatory filing or market data feed.

Answer

Confidence indicators

Answer

Model Cards

Answer

Chain-of-thought reasoning

Question 7

A global company deploys a generative AI chatbot in the European Union. They must ensure compliance with GDPR regarding user data used for fine-tuning the model. What is the MOST important requirement they must fulfill?

Accepted Answer

Obtain explicit consent from data subjects for using their data in fine-tuning. Under GDPR, using personal data for fine-tuning a generative AI model constitutes a new processing purpose that requires a lawful basis. Explicit consent (Article 7 and Article 9) is the most robust basis when relying on consent, as it must be freely given, specific, informed, and unambiguous. Without explicit consent, the company risks violating data minimization and purpose limitation principles, even if other anonymization or storage measures are applied.

Answer

Use only synthetic data for fine-tuning to avoid GDPR issues

Answer

Store all fine-tuned models only on US-based servers

Answer

Anonymize all data before fine-tuning, regardless of consent

Question 8

A researcher wants to detect whether text was generated by an AI model to identify potential misinformation. Which Google technology is specifically designed for this purpose?

Accepted Answer

SynthID. SynthID is a Google DeepMind technology specifically designed to watermark and detect AI-generated text, images, audio, and video. It embeds an imperceptible digital watermark directly into the output of generative models, allowing for reliable detection even after modifications like cropping or compression. This makes it the correct tool for identifying potential misinformation from AI-generated text.

Answer

Model Cards

Answer

PAIR Explorables

Answer

Datasheets for Datasets

Question 9

What is the primary purpose of Google's Content Safety filters in Vertex AI?

Accepted Answer

To block generated content that contains hate speech, violence, or sexually explicit material. Google's Content Safety filters in Vertex AI are designed to block generated content that violates safety policies, specifically targeting hate speech, violence, and sexually explicit material. This is a core component of responsible AI deployment, ensuring that model outputs adhere to ethical guidelines and legal requirements. The filters operate by analyzing the generated text or images against predefined safety categories, not by assessing data quality or source curation.

Answer

To filter out low-quality training data

Answer

To ensure the model only generates content from a curated set of sources

Answer

To improve the model's accuracy on safe content

Question 10

A company is using a generative AI model to automatically screen job applications. They want to ensure the model does not discriminate based on gender or ethnicity. Which of the following actions should they take as part of responsible AI?

Accepted Answer

Evaluate the model's hiring decisions for disparate impact across gender and ethnicity. Evaluating the model's decisions for disparate impact using protected attributes is a key step in detecting and mitigating unfair bias.

Answer

Use a more powerful model to improve accuracy, as bias decreases with better performance

Answer

Blindly apply a fairness algorithm without understanding the context

Answer

Remove all demographic data from the training set to prevent the model from learning biases

Question 11

A startup uses a generative AI model to create marketing content. They plan to sell the generated content commercially. What is the most important legal consideration regarding copyright?

Accepted Answer

They must verify the training data provenance to ensure no copyrighted material was used without permission. Option D is correct because under current copyright law, the user of a generative AI system is typically considered the author of the output, but only if the training data was lawfully obtained. If the model was trained on copyrighted works without permission, the generated content may be considered a derivative work, exposing the startup to infringement liability. Verifying training data provenance is therefore the most critical legal step before commercializing AI-generated content.

Answer

The model automatically owns the copyright to all generated content

Answer

They should apply SynthID watermarking to all generated content

Answer

They can use any generated content freely as long as they attribute the model

Question 12

According to Google's AI Principles, AI systems should be 'accountable to people.' What does this principle primarily require?

Accepted Answer

Organizations should provide avenues for feedback and recourse when AI systems cause harm. Accountability means providing mechanisms for feedback and recourse when AI systems cause harm or make mistakes.

Answer

AI systems should always have a human monitor present during operation

Answer

The AI system should be able to explain all its decisions to end users

Answer

AI systems should be designed to maximize shareholder value

Question 13

A data architect is designing a system that uses a generative AI model to summarize customer support transcripts. The system must comply with GDPR and company policy requiring data residency in the EU. Which TWO controls should they implement?

Accepted Answer

Configure the Vertex AI endpoint to use an EU region for data processing. Option D is correct because configuring the Vertex AI endpoint to use an EU region ensures that all data processing, including model inference and any intermediate data handling, occurs within the EU, satisfying GDPR data residency requirements. This control directly enforces geographic data localization without relying on data transfer mechanisms like Standard Contractual Clauses (SCCs).

Answer

Enable prompt logging and retention for 5 years for auditing

Answer

Use a model that has been fine-tuned on all customer transcripts globally

Answer

Use a non-EU region to reduce latency

Question 14

A product manager wants to transparently communicate the capabilities and limitations of their new generative AI feature to stakeholders. Which THREE resources from the Responsible AI Toolkit should they use?

Accepted Answer

Model Cards. Model Cards document model details, Datasheets for Datasets document data, and the People + AI Guidebook provides best practices for communication. PAIR Explorables are for education, not documentation.

Answer

SynthID

Answer

PAIR Explorables

Question 15

A company is deploying a generative AI model for medical diagnosis assistance. To comply with both Google's AI Principles and emerging regulations (e.g., EU AI Act), they must ensure appropriate human oversight. Which THREE measures should they implement?

Accepted Answer

Require a human clinician to review all AI-generated diagnoses before acting on them. Option C is correct because requiring a human clinician to review all AI-generated diagnoses before acting on them directly implements the human oversight mandate of Google's AI Principles (specifically the 'be accountable to people' principle) and the EU AI Act's requirement for high-risk AI systems to have meaningful human review. This ensures that the model's output is validated by a domain expert, mitigating risks of false positives or negatives that could harm patients.

Answer

Use a model with high confidence scores to bypass human review

Answer

Allow the AI to act autonomously for low-risk cases to reduce workload