- A
Use data augmentation to create synthetic data and discard original data
Why wrong: While synthetic data can help privacy, simply discarding original data without consent may not be compliant.
- B
Obtain explicit consent from users whose data is used for fine-tuning
Consent ensures transparency and user control, aligning with privacy principles.
- C
Anonymize the data and publish it for public research
Why wrong: Publishing data, even anonymized, may not align with the intended purpose and consent.
- D
Retain all fine-tuning data indefinitely for model improvement
Why wrong: Indefinite retention violates data minimization and may increase privacy risk.
Question 358 of 997
Generative AI Leader Responsible AI and Data Governance Practice Question
This Generative AI Leader practice question tests your understanding of responsible ai and data governance. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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
✓
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use data augmentation to create synthetic data and discard original data
Why it's wrong here
While synthetic data can help privacy, simply discarding original data without consent may not be compliant.
- ✓
Obtain explicit consent from users whose data is used for fine-tuning
Why this is correct
Consent ensures transparency and user control, aligning with privacy principles.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Anonymize the data and publish it for public research
Why it's wrong here
Publishing data, even anonymized, may not align with the intended purpose and consent.
- ✗
Retain all fine-tuning data indefinitely for model improvement
Why it's wrong here
Indefinite retention violates data minimization and may increase privacy risk.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'anonymization' (Option C) with sufficient privacy protection, but Google's privacy design principles require explicit consent even for anonymized data when used for fine-tuning, as the model can still memorize patterns or be subject to re-identification attacks.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI's fine-tuning service uses customer-provided datasets to adjust model weights via techniques like LoRA or full fine-tuning, and Google's privacy design principles are enforced through data governance policies that require explicit consent as part of the data ingestion pipeline. In a real-world scenario, a company using customer chat logs to fine-tune a support model must implement a consent management system (e.g., via Cloud Data Loss Prevention or custom APIs) to tag and filter data based on user opt-in status before training, ensuring compliance with GDPR or CCPA.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Responsible AI and Data Governance — study guide chapter
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Responsible AI and Data Governance — This question tests Responsible AI and Data Governance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this Generative AI Leader question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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