- A
Training a model using data from multiple countries governed by a federal legal system
Why wrong: Legal jurisdiction is governance — federated learning is a distributed training technique that keeps raw data local.
- B
Distributed training where devices share model updates (not raw data) — enabling privacy-preserving collaborative learning
Federated learning: local training + weight sharing — multiple organisations can improve a shared model without sharing sensitive raw data.
- C
A training approach where a federal government agency controls access to all training data
Why wrong: Government data control is policy — federated learning is a technical architecture for privacy-preserving distributed model training.
- D
Combining predictions from models trained independently at multiple research institutions
Why wrong: Combining independent predictions is ensemble learning — federated learning shares model updates during training across distributed participants.
Quick Answer
The answer is federated learning, a distributed machine learning technique where devices share model updates rather than raw data to enable privacy-preserving AI. This approach is correct because it keeps sensitive information local—only gradients or weights are sent to a central server for aggregation, so raw data never leaves the device. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how Azure Machine Learning supports privacy without centralizing data, often appearing in scenarios like healthcare or mobile keyboard prediction where data cannot be moved due to regulations. A common trap is confusing federated learning with traditional centralized training; remember the key distinction is that the model travels to the data, not the other way around. For a quick memory tip, think “federated” like a federation of states—each keeps its own data but collaborates on a shared model.
AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
This AI-900 practice question tests your understanding of describe fundamental principles of machine learning on azure. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
What is 'federated learning' and when is it used for privacy-preserving AI?
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
Distributed training where devices share model updates (not raw data) — enabling privacy-preserving collaborative learning
Federated learning is a distributed machine learning technique where the model is trained across multiple decentralized devices or servers holding local data, without exchanging the raw data itself. Instead, only model updates (e.g., gradients or weights) are shared with a central server, which aggregates them to improve the global model. This approach preserves privacy because sensitive data never leaves the local device, making it ideal for scenarios like healthcare, finance, or mobile keyboard prediction where data cannot be centralized due to regulatory or privacy constraints.
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.
- ✗
Training a model using data from multiple countries governed by a federal legal system
Why it's wrong here
Legal jurisdiction is governance — federated learning is a distributed training technique that keeps raw data local.
- ✓
Distributed training where devices share model updates (not raw data) — enabling privacy-preserving collaborative learning
Why this is correct
Federated learning: local training + weight sharing — multiple organisations can improve a shared model without sharing sensitive raw data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
A training approach where a federal government agency controls access to all training data
Why it's wrong here
Government data control is policy — federated learning is a technical architecture for privacy-preserving distributed model training.
- ✗
Combining predictions from models trained independently at multiple research institutions
Why it's wrong here
Combining independent predictions is ensemble learning — federated learning shares model updates during training across distributed participants.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'federated' with 'federal' or 'government-controlled' systems, or mistake federated learning for simple ensemble methods, when the core concept is decentralized training with privacy-preserving model update sharing.
Detailed technical explanation
How to think about this question
Under the hood, federated learning typically uses the Federated Averaging (FedAvg) algorithm, where each client performs local stochastic gradient descent (SGD) on its private data and sends the updated model weights to a central server, which averages them to produce a new global model. A subtle but critical behavior is that the server never sees raw data, but model updates can still leak information through gradient inversion attacks, so techniques like differential privacy or secure aggregation (e.g., using homomorphic encryption or secure multi-party computation) are often added for stronger privacy guarantees. A real-world scenario is Google's Gboard keyboard, which uses federated learning to improve next-word prediction without uploading users' typing history to the cloud.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe fundamental principles of machine learning on Azure — This question tests Describe fundamental principles of machine learning on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Distributed training where devices share model updates (not raw data) — enabling privacy-preserving collaborative learning — Federated learning is a distributed machine learning technique where the model is trained across multiple decentralized devices or servers holding local data, without exchanging the raw data itself. Instead, only model updates (e.g., gradients or weights) are shared with a central server, which aggregates them to improve the global model. This approach preserves privacy because sensitive data never leaves the local device, making it ideal for scenarios like healthcare, finance, or mobile keyboard prediction where data cannot be centralized due to regulatory or privacy constraints.
What should I do if I get this AI-900 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: Jun 11, 2026
This AI-900 practice question is part of Courseiva's free Microsoft 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 AI-900 exam.
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