- A
Differential privacy
Why wrong: Differential privacy adds noise to protect privacy but can be applied to the model update; it does not inherently keep data local.
- B
Federated learning
Federated learning trains models on local data and only shares model updates, keeping raw data on-site.
- C
Data anonymisation
Why wrong: Anonymisation removes identifiers but still requires sharing the dataset.
- D
Data pseudonymisation
Why wrong: Pseudonymisation replaces identifiers but still shares data.
Question 444 of 1,000
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 hospital wants to train a diagnostic AI model using data from multiple hospitals without sharing raw patient data. Which privacy-preserving technique allows collaborative training while keeping data local?
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
✓
Federated learning
Federated learning is the correct technique because it enables multiple hospitals to collaboratively train a shared diagnostic AI model without exchanging raw patient data. Instead, each hospital trains a local model on its own data, and only encrypted model updates (e.g., gradients or weights) are sent to a central server for aggregation. This keeps all sensitive patient information local, directly addressing the requirement of data locality while still benefiting from collective learning.
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.
- ✗
Differential privacy
Why it's wrong here
Differential privacy adds noise to protect privacy but can be applied to the model update; it does not inherently keep data local.
- ✓
Federated learning
Why this is correct
Federated learning trains models on local data and only shares model updates, keeping raw data on-site.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Data anonymisation
Why it's wrong here
Anonymisation removes identifiers but still requires sharing the dataset.
- ✗
Data pseudonymisation
Why it's wrong here
Pseudonymisation replaces identifiers but still shares data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between techniques that modify data before sharing (like anonymisation or pseudonymisation) versus techniques that keep data local and share only model parameters (like federated learning), so the trap here is assuming that anonymising or pseudonymising data satisfies the 'keep data local' requirement when it actually still involves data leaving the hospital.
Detailed technical explanation
How to think about this question
In federated learning, the central server typically uses the Federated Averaging (FedAvg) algorithm, where each client performs multiple local stochastic gradient descent (SGD) steps before sending weight updates. A subtle behavior is that even model updates can leak information about local data (e.g., via gradient inversion attacks), so techniques like secure aggregation (using secure multi-party computation) or differential privacy are often layered on top to provide stronger privacy guarantees. In a real-world hospital scenario, this prevents a malicious server from reconstructing patient records from the aggregated model updates.
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 practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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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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: Federated learning — Federated learning is the correct technique because it enables multiple hospitals to collaboratively train a shared diagnostic AI model without exchanging raw patient data. Instead, each hospital trains a local model on its own data, and only encrypted model updates (e.g., gradients or weights) are sent to a central server for aggregation. This keeps all sensitive patient information local, directly addressing the requirement of data locality while still benefiting from collective learning.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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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