- A
Use a smaller test set.
Why wrong: A smaller test set would provide a less reliable evaluation of generalization and does not address overfitting during training.
- B
Apply feature scaling only.
Why wrong: Feature scaling (e.g., normalization) helps gradient descent converge but does not penalize large coefficients or directly reduce overfitting.
- C
Use a regularization algorithm like Lasso (L1).
Regularization adds a penalty for large coefficients (L1 shrinkage), which forces some coefficients to zero and reduces model complexity, effectively combating overfitting.
- D
Increase the number of training epochs.
Why wrong: More training epochs can cause the model to overfit further by learning the training data more precisely, increasing the gap between training and test error.
Quick Answer
The answer is to use a regularization algorithm like Lasso (L1). This technique directly reduces overfitting in regression with regularization by adding a penalty equal to the absolute value of the coefficient magnitudes, which shrinks some coefficients to zero and performs automatic feature selection. When a model has very low training error but high test error, it has memorized noise rather than learning general patterns, and penalizing large coefficients forces the model to simplify. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your understanding of how to combat overfitting in Azure Machine Learning, often appearing as a choice between L1 (Lasso) and L2 (Ridge) regularization. A common trap is confusing L2 regularization, which shrinks coefficients but never to zero, with L1’s ability to eliminate irrelevant features entirely. Memory tip: Lasso “L1” can “Lop off” coefficients to zero, leaving only the most important predictors.
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. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 training a regression model to predict house prices in Azure Machine Learning. The model uses features like square footage, number of bedrooms, and location (zip code). The data scientist notices that the model has a very low error on the training data but a high error on the test data. Which technique should the data scientist apply during model training to reduce overfitting by penalizing large coefficients?
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
Use a regularization algorithm like Lasso (L1).
Option C is correct because Lasso (L1) regularization adds a penalty equal to the absolute value of the magnitude of coefficients, which can shrink some coefficients to zero, effectively performing feature selection and reducing overfitting. This directly addresses the problem of large coefficients causing the model to fit noise in the training data, leading to high test error.
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.
- ✗
Use a smaller test set.
Why it's wrong here
A smaller test set would provide a less reliable evaluation of generalization and does not address overfitting during training.
- ✗
Apply feature scaling only.
Why it's wrong here
Feature scaling (e.g., normalization) helps gradient descent converge but does not penalize large coefficients or directly reduce overfitting.
- ✓
Use a regularization algorithm like Lasso (L1).
Why this is correct
Regularization adds a penalty for large coefficients (L1 shrinkage), which forces some coefficients to zero and reduces model complexity, effectively combating overfitting.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of training epochs.
Why it's wrong here
More training epochs can cause the model to overfit further by learning the training data more precisely, increasing the gap between training and test error.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse regularization with feature scaling or training duration, not realizing that only regularization directly penalizes large coefficient magnitudes to combat overfitting.
Detailed technical explanation
How to think about this question
L1 regularization (Lasso) adds a penalty term λ∑|w_i| to the loss function, where λ controls the strength of regularization. This encourages sparsity in the model, meaning many feature coefficients become exactly zero, which is particularly useful when dealing with high-dimensional data or when feature selection is desired. In Azure Machine Learning, this can be implemented using the 'Lasso' estimator or by setting the 'penalty' parameter to 'l1' in linear models like SGDRegressor.
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: Use a regularization algorithm like Lasso (L1). — Option C is correct because Lasso (L1) regularization adds a penalty equal to the absolute value of the magnitude of coefficients, which can shrink some coefficients to zero, effectively performing feature selection and reducing overfitting. This directly addresses the problem of large coefficients causing the model to fit noise in the training data, leading to high test error.
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
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 →
Same concept, more angles
2 more ways this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is training a regression model to predict house prices. The model performs near perfectly on the training data but poorly on a held-out test set. The scientist suspects the model is memorizing the training data instead of learning general patterns. Which technique is most appropriate to directly address this issue?
hard- A.Increase the size of the training dataset
- B.Increase the complexity of the model (e.g., add more features)
- ✓ C.Apply L2 regularization to the model
- D.Switch to a different regression algorithm
Why C: L2 regularization (also known as Ridge regularization) directly addresses overfitting by adding a penalty term proportional to the square of the model weights to the loss function. This discourages the model from assigning excessively large coefficients to features, forcing it to learn simpler, more general patterns rather than memorizing noise in the training data.
Variation 2. A data scientist trains a regression model to predict house prices using features like square footage, number of bedrooms, and location. The model achieves very high accuracy on the training data but performs poorly on a held-out test set. Which technique should the data scientist apply to reduce overfitting?
medium- A.Increase the number of features
- B.Decrease the training data size
- ✓ C.Use regularization
- D.Increase the number of training epochs
Why C: Regularization (Option C) is the correct technique to reduce overfitting because it adds a penalty term to the loss function (e.g., L1 or L2 regularization), which discourages the model from learning overly complex patterns that fit noise in the training data. This helps the model generalize better to unseen data, such as the held-out test set, by constraining the magnitude of feature weights.
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Last reviewed: Jun 11, 2026
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