- A
De-identify the transcripts by removing or masking PII before fine-tuning
De-identification minimizes privacy risk and aligns with privacy design principles.
- B
Use a model that has already been trained on similar data to avoid fine-tuning
Why wrong: This does not address the PII already present in the transcripts if they are used for evaluation or other purposes.
- C
Obtain consent from all customers whose data appears in the transcripts
Why wrong: Consent is important but may be impractical or insufficient; de-identification is a stronger privacy safeguard.
- D
Fine-tune the model directly on the transcripts, as the model will not memorize exact data
Why wrong: Models can memorize training data, including PII, which poses privacy risks and may violate principles.
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 large language model on proprietary customer support transcripts. The transcripts contain personally identifiable information (PII) such as names and email addresses. What is the BEST practice to comply with Google's AI Principles on privacy?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
De-identify the transcripts by removing or masking PII before fine-tuning
Google's AI Principles include incorporating privacy design principles. The best practice is to de-identify the training data by removing or masking PII before fine-tuning, reducing privacy risk.
Key principle: Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
De-identify the transcripts by removing or masking PII before fine-tuning
Why this is correct
De-identification minimizes privacy risk and aligns with privacy design principles.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
CIDR notation defines the prefix length.
- ✗
Use a model that has already been trained on similar data to avoid fine-tuning
Why it's wrong here
This does not address the PII already present in the transcripts if they are used for evaluation or other purposes.
- ✗
Obtain consent from all customers whose data appears in the transcripts
Why it's wrong here
Consent is important but may be impractical or insufficient; de-identification is a stronger privacy safeguard.
- ✗
Fine-tune the model directly on the transcripts, as the model will not memorize exact data
Why it's wrong here
Models can memorize training data, including PII, which poses privacy risks and may violate principles.
Common exam traps
Common exam trap: usable hosts are not the same as total addresses
Subnetting questions often tempt you into counting all addresses. In normal IPv4 subnets, the network and broadcast addresses are not usable host addresses.
Detailed technical explanation
How to think about this question
Subnetting questions test whether you can identify the network, broadcast address, usable range, mask and correct subnet. Slow down enough to calculate the block size correctly.
KKey Concepts to Remember
- CIDR notation defines the prefix length.
- Block size helps identify subnet boundaries.
- Network and broadcast addresses are not usable hosts in normal IPv4 subnets.
- The required host count determines the smallest suitable subnet.
TExam Day Tips
- Write the block size before choosing the subnet.
- Check whether the question asks for hosts, subnets or a specific address range.
- Do not confuse /24, /25, /26 and /27 host counts.
Key takeaway
Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
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. Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets. 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.
Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related Generative AI Leader subnetting questions on CIDR, address ranges, and subnet selection.
- →
Responsible AI and Data Governance — study guide chapter
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Responsible AI and Data Governance practice questions
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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 — CIDR notation defines the prefix length..
What is the correct answer to this question?
The correct answer is: De-identify the transcripts by removing or masking PII before fine-tuning — Google's AI Principles include incorporating privacy design principles. The best practice is to de-identify the training data by removing or masking PII before fine-tuning, reducing privacy risk.
What should I do if I get this Generative AI Leader question wrong?
Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related Generative AI Leader subnetting questions on CIDR, address ranges, and subnet selection.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
CIDR notation defines the prefix length.
About these practice questions
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Last reviewed: Jul 4, 2026
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.
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