- A
Standardising input features to the same scale before training
Why wrong: Feature scaling is preprocessing (normalisation/standardisation) — regularisation is a penalty on model complexity during training.
- B
Adding a penalty to the loss function to discourage overly complex models and reduce overfitting
Regularisation (L1/L2) penalises large weights, preventing overfitting by favouring simpler models that generalise better.
- C
Applying government regulations to ensure AI models comply with data privacy laws
Why wrong: Legal compliance is governance — regularisation is a mathematical technique for controlling model complexity.
- D
Converting irregular training data shapes into a uniform format for the algorithm
Why wrong: Data shape standardisation is preprocessing — regularisation is a loss function modification that constrains model weight magnitudes.
Quick Answer
The answer is adding a penalty to the loss function to discourage overly complex models and reduce overfitting. This works because regularization introduces a cost for large or numerous coefficients, forcing the model to simplify its learned patterns rather than memorizing noise in the training data. By penalizing complexity, the model generalizes better to unseen data, directly solving the problem of overfitting where a model performs well on training data but poorly on new inputs. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how to control model complexity, often appearing in questions about configuring algorithms like linear regression or neural networks in Azure Machine Learning. A common trap is confusing regularization with feature selection—while L1 regularization (Lasso) can zero out features, the core purpose is always preventing overfitting. Remember the mnemonic: “Penalty for Complexity Prevents Overfitting” to recall that regularization adds a penalty term to the loss function.
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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 'regularisation' in machine learning and what problem does it solve?
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
Adding a penalty to the loss function to discourage overly complex models and reduce overfitting
Regularisation is a technique used in machine learning to prevent overfitting by adding a penalty term to the loss function. This penalty discourages the model from learning overly complex patterns, such as large or numerous coefficients, which helps the model generalise better to unseen data. In Azure Machine Learning, regularisation parameters like L1 (Lasso) or L2 (Ridge) can be configured in algorithms such as linear regression or neural networks to control model complexity.
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.
- ✗
Standardising input features to the same scale before training
Why it's wrong here
Feature scaling is preprocessing (normalisation/standardisation) — regularisation is a penalty on model complexity during training.
- ✓
Adding a penalty to the loss function to discourage overly complex models and reduce overfitting
Why this is correct
Regularisation (L1/L2) penalises large weights, preventing overfitting by favouring simpler models that generalise better.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Applying government regulations to ensure AI models comply with data privacy laws
Why it's wrong here
Legal compliance is governance — regularisation is a mathematical technique for controlling model complexity.
- ✗
Converting irregular training data shapes into a uniform format for the algorithm
Why it's wrong here
Data shape standardisation is preprocessing — regularisation is a loss function modification that constrains model weight magnitudes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse regularisation with data preprocessing steps like normalisation or reshaping, because both involve modifying data or model parameters, but regularisation specifically targets overfitting by penalising complexity, not by altering input data format or scale.
Detailed technical explanation
How to think about this question
Under the hood, regularisation works by adding a term like λ * Σ|w| (L1) or λ * Σw² (L2) to the loss function, where λ is a hyperparameter controlling the penalty strength. L1 regularisation can drive some feature weights to zero, effectively performing feature selection, while L2 regularisation shrinks weights uniformly but never to zero. In Azure ML, you can tune λ via hyperparameter sweeps to balance bias and variance, which is critical in high-dimensional datasets like genomic or text data.
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: Adding a penalty to the loss function to discourage overly complex models and reduce overfitting — Regularisation is a technique used in machine learning to prevent overfitting by adding a penalty term to the loss function. This penalty discourages the model from learning overly complex patterns, such as large or numerous coefficients, which helps the model generalise better to unseen data. In Azure Machine Learning, regularisation parameters like L1 (Lasso) or L2 (Ridge) can be configured in algorithms such as linear regression or neural networks to control model complexity.
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.
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Last reviewed: Jun 11, 2026
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