- A
Train the model on a small subset of data to reduce exposure
Why wrong: Training on a subset may reduce accuracy but does not provide formal differential privacy guarantees.
- B
Remove all personally identifiable information (PII) from the dataset
Why wrong: Removing PII is an anonymization technique, not differential privacy.
- C
Aggregate data into groups before training
Why wrong: Aggregation can reduce granularity but does not provide differential privacy guarantees.
- D
Set a privacy budget (epsilon) to limit information leakage
The privacy budget epsilon quantifies the privacy guarantee and is a core concept of differential privacy.
- E
Add noise to the training data to mask individual contributions
Adding noise to the data or gradients is a standard method to achieve differential privacy.
AI0-001 AI Governance and Ethics Practice Question
This AI0-001 practice question tests your understanding of ai governance and ethics. 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 data scientist is using differential privacy to protect individual privacy in a training dataset. Which TWO actions are correct implementations of differential privacy?
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
Set a privacy budget (epsilon) to limit information leakage
Option D is correct because setting a privacy budget (epsilon) is a core mechanism in differential privacy that quantifies and limits the amount of information leaked about any individual in the dataset. By controlling epsilon, the data scientist can formally bound the privacy loss, ensuring that the model's outputs do not reveal whether any specific individual's data was included in training.
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.
- ✗
Train the model on a small subset of data to reduce exposure
Why it's wrong here
Training on a subset may reduce accuracy but does not provide formal differential privacy guarantees.
- ✗
Remove all personally identifiable information (PII) from the dataset
Why it's wrong here
Removing PII is an anonymization technique, not differential privacy.
- ✗
Aggregate data into groups before training
Why it's wrong here
Aggregation can reduce granularity but does not provide differential privacy guarantees.
- ✓
Set a privacy budget (epsilon) to limit information leakage
Why this is correct
The privacy budget epsilon quantifies the privacy guarantee and is a core concept of differential privacy.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Add noise to the training data to mask individual contributions
Why this is correct
Adding noise to the data or gradients is a standard method to achieve differential privacy.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that removing PII or using data aggregation alone constitutes differential privacy, when in fact differential privacy requires a formal mathematical framework with noise addition and a privacy budget parameter.
Detailed technical explanation
How to think about this question
Differential privacy works by adding calibrated noise (e.g., Laplace or Gaussian noise) to query results or training gradients, where the noise scale is determined by the privacy budget epsilon and the sensitivity of the function. A smaller epsilon provides stronger privacy but reduces model accuracy, creating a fundamental trade-off that must be managed iteratively across multiple training epochs. In practice, frameworks like TensorFlow Privacy implement this via differentially private stochastic gradient descent (DP-SGD), which clips gradients and adds noise during backpropagation.
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 network engineer segments a warehouse floor into three subnets: 20 scanners, 5 printers, and 2 management hosts. Picking the wrong mask wastes addresses or leaves too few usable hosts. Exam questions test whether you can apply CIDR notation, calculate block size, and identify the correct usable-host range for a given prefix.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Governance and Ethics — This question tests AI Governance and Ethics — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Set a privacy budget (epsilon) to limit information leakage — Option D is correct because setting a privacy budget (epsilon) is a core mechanism in differential privacy that quantifies and limits the amount of information leaked about any individual in the dataset. By controlling epsilon, the data scientist can formally bound the privacy loss, ensuring that the model's outputs do not reveal whether any specific individual's data was included in training.
What should I do if I get this AI0-001 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.
About these practice questions
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Last reviewed: Jul 4, 2026
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.
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