Generative AI Leader Responsible AI and Data Governance Practice Test 1 — 15 Questions

Question 1

A healthcare startup is developing a generative AI system to assist doctors in diagnosing rare diseases. According to Google's AI Principles, what is the MOST important requirement before deployment?

Accepted Answer

The system must include a mechanism for human review of all diagnostic suggestions. Google's AI Principles state that AI systems should be built and tested for safety, especially in high-stakes domains like healthcare, where human oversight is critical.

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The model must achieve at least 99% accuracy on a held-out test set

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The model must be trained on the most recent medical literature

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The startup must publish the model's architecture in a peer-reviewed journal

Question 2

A financial services company uses a generative AI model to summarize customer complaints. They notice that summaries for certain demographics consistently omit negative sentiment. Which responsible AI practice should they apply FIRST to address this bias?

Accepted Answer

Evaluate the model's outputs for bias using a diverse test set that represents all customer demographics. Evaluating the model's outputs for bias using diverse test sets is essential to identify and mitigate unfair bias, as outlined in Google's AI Principles.

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Store all prompts and responses in Cloud Logging for auditing

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Implement SynthID watermarking on all generated summaries

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Reduce the temperature parameter of the LLM to 0.1 to make outputs more deterministic

Question 3

A media company wants to use a generative AI model to create marketing copy that includes citations to original sources. Which feature should they enable to ensure the model provides accurate attributions?

Accepted Answer

Grounding. Grounding allows the model to cite sources, improving explainability and trustworthiness by connecting outputs to verifiable information.

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Confidence indicators

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Chain-of-thought reasoning

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Safety filters

Question 4

A multinational corporation deploys a generative AI chatbot for customer support in the EU. They must ensure compliance with GDPR regarding user data used for fine-tuning. Which data governance practice is REQUIRED?

Accepted Answer

Implement a mechanism to delete specific user data from the fine-tuning dataset upon user request. Under GDPR, users have the right to erasure (Article 17), which requires that their data be deleted upon request. For a generative AI chatbot fine-tuned on user data, this means the organization must have a mechanism to remove specific user data from the training dataset, as the model may have memorized that data. Option C directly addresses this right-to-deletion requirement, making it the mandatory practice.

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Store user data only in the EU region to comply with data residency requirements

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Obtain explicit consent from users for their data to be used in fine-tuning

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Anonymize all user data before using it for fine-tuning

Question 5

A generative AI model used for generating product descriptions occasionally outputs hateful content. The company wants a scalable solution to block such content without modifying the model. Which Google Cloud feature should they use?

Accepted Answer

Custom content controls in Vertex AI. Google's safety filters are designed to block harmful content categories, including hate speech, and can be applied at inference time without retraining the model.

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Retraining the model with a more diverse dataset

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Model Cards to document known biases

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Datasheets for Datasets to improve training data quality

Question 6

An AI research lab wants to publish a model card for their new generative AI model. According to Google's Responsible AI practices, which information is ESSENTIAL to include in the model card?

Accepted Answer

The intended use, performance metrics, and known limitations. Model Cards should disclose intended use, performance metrics, and known limitations to promote transparency and accountability.

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The exact architecture and hyperparameters used

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A list of all training hardware and software versions

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The names and salaries of the development team

Question 7

A startup is developing an AI tool that generates legal contract summaries. Lawyers will use these summaries for advice. What human oversight mechanism is MOST critical for responsible deployment?

Accepted Answer

Require lawyers to review and approve each AI-generated summary before use. Option A is correct because in high-stakes domains like legal contract summarization, the AI output must be validated by a qualified human expert before use. This ensures accountability and mitigates risks of hallucinated clauses or misinterpretations that could lead to legal liability. A confidence score or automated flagging alone is insufficient because even high-confidence outputs can contain subtle errors that only a trained lawyer can catch.

Answer

Provide a confidence score for each summary but allow lawyers to bypass it

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Use a second LLM to verify the first LLM's summaries automatically

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Automatically flag summaries that contain low-confidence indicators for review

Question 8

A company wants to ensure that AI-generated images can be traced back to their origin to prevent misuse. Which Google technology provides invisible watermarking for AI-generated content?

Accepted Answer

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

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Datasheets for Datasets

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Model Cards

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People + AI Guidebook

Question 9

A company is fine-tuning a generative AI model on proprietary customer data. They are concerned about copyright and IP issues when using the model commercially. What is the BEST practice to mitigate these risks?

Accepted Answer

Only use data that is explicitly licensed for commercial use and document its provenance. Maintaining clear documentation of training data provenance and licensing is crucial for IP compliance, as recommended by Google's AI governance.

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Apply SynthID to all generated content to prove origin

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Include a disclaimer on all outputs that the company is not liable for IP infringement

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Use a model trained on publicly available data only

Question 10

A cloud architect is designing a generative AI pipeline that must comply with the EU AI Act for high-risk AI systems. Which of the following is a mandatory requirement under the Act?

Accepted Answer

The system must undergo a conformity assessment before deployment. Under the EU AI Act, high-risk AI systems must undergo a conformity assessment before deployment to ensure compliance with requirements such as risk management, data governance, and transparency. This is a mandatory procedural step, not a performance metric or specific reasoning technique. The assessment may involve self-evaluation or third-party review depending on the system's risk category.

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The system must be explainable using chain-of-thought reasoning

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The system must achieve a minimum accuracy of 90% on validation data

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The system must be trained on data that is representative of the target population

Question 11

A product manager wants to communicate the limitations of a new generative AI feature to stakeholders. According to Google's People + AI Guidebook, what is the BEST approach?

Accepted Answer

Provide clear examples of the AI's capabilities and failure modes. The People + AI Guidebook emphasizes setting appropriate expectations by clearly communicating what the AI can and cannot do.

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Highlight only the successful use cases to build excitement

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Promise that future versions will overcome all limitations

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Share a technical paper detailing the model architecture

Question 12

A research team uses a generative AI model to analyze historical texts. They want to provide users with insight into the model's reasoning process. Which explainability technique should they implement?

Accepted Answer

Chain-of-thought reasoning. Chain-of-thought reasoning provides step-by-step explanations of how the model arrived at its conclusion, enhancing transparency.

Answer

Grounding

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Confidence indicators

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Safety filters

Question 13

A company is deploying a generative AI system for resume screening. They want to ensure fairness and avoid bias. Which TWO actions should they take? (Choose 2)

Accepted Answer

Evaluate the model's decisions across gender and ethnicity groups using a diverse test set. Evaluating bias with diverse test sets and ensuring representative training data are key steps to mitigate bias, as per Google's AI Principles.

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Apply SynthID watermarking to all generated decisions

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Remove protected attributes like gender and race from training data

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Use a larger model to improve accuracy

Question 14

A healthcare organization wants to use generative AI for clinical decision support. They must comply with regulatory requirements for high-risk AI systems. Which THREE measures should they implement? (Choose 3)

Accepted Answer

Implement a human-in-the-loop review for all AI-generated recommendations. Human oversight, risk management, and transparency are key regulatory requirements for high-risk AI systems.

Answer

Use a model that has been fine-tuned on proprietary hospital data

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Deploy the system on-premises only to ensure data privacy

Question 15

A company uses a generative AI model to produce financial reports. They want to ensure content safety and prevent the generation of harmful or misleading information. Which TWO Google Cloud features should they configure? (Choose 2)

Accepted Answer

Enable Google's safety filters for hate speech, violence, and sexual content. Option C is correct because Google's safety filters in Vertex AI provide built-in, configurable protections against hate speech, violence, and sexual content, directly addressing content safety requirements for generative AI outputs. These filters are part of Google's Responsible AI toolkit and can be enabled at the model deployment level to automatically block harmful content without requiring custom development.

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Fine-tune the model on a dataset of approved financial reports

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Log all prompts and responses in Cloud Logging

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Use SynthID to watermark all outputs