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. A key principle to apply: l2 regularization adds a penalty based on the square of coefficient magnitudes.. 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. 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?
Why wrong: More data can help, but it is not a direct modeling technique; regularization is applied at training time to constrain the model and prevent memorization.
B
Increase the complexity of the model (e.g., add more features)
Why wrong: Increasing complexity typically makes overfitting worse, as the model becomes more capable of memorizing noise.
C
Apply L2 regularization to the model
L2 regularization penalizes large coefficients, reducing the model's tendency to fit noise and improving generalization.
D
Switch to a different regression algorithm
Why wrong: Changing algorithms may help, but regularization is a direct and standard technique for reducing overfitting in regression models.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply L2 regularization to the model
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.
Key principle: L2 regularization adds a penalty based on the square of coefficient magnitudes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Increase the size of the training dataset
Why it's wrong here
More data can help, but it is not a direct modeling technique; regularization is applied at training time to constrain the model and prevent memorization.
✗
Increase the complexity of the model (e.g., add more features)
Why it's wrong here
Increasing complexity typically makes overfitting worse, as the model becomes more capable of memorizing noise.
✓
Apply L2 regularization to the model
Why this is correct
L2 regularization penalizes large coefficients, reducing the model's tendency to fit noise and improving generalization.
Related concept
L2 regularization adds a penalty based on the square of coefficient magnitudes.
✗
Switch to a different regression algorithm
Why it's wrong here
Changing algorithms may help, but regularization is a direct and standard technique for reducing overfitting in regression models.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'increasing data' (Option A) as the universal fix for overfitting, but the question specifically asks for a technique that directly addresses memorization, which is regularization, not data augmentation.
Detailed technical explanation
How to think about this question
L2 regularization works by adding a term λ * Σ(w_i²) to the cost function, where λ is a hyperparameter controlling the strength of regularization. During gradient descent, this causes the weight updates to include a decay factor, shrinking weights toward zero but never exactly to zero, which reduces variance without eliminating features. In Azure Machine Learning, you can implement L2 regularization via the 'regularization_rate' parameter in algorithms like LinearRegression or SGDRegressor.
KKey Concepts to Remember
L2 regularization adds a penalty based on the square of coefficient magnitudes.
It encourages smaller, more distributed weights, simplifying the model.
L2 regularization helps prevent overfitting by reducing model complexity.
It is a direct technique to improve model generalization to new data.
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
L2 regularization adds a penalty based on the square of coefficient magnitudes.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. L2 regularization adds a penalty based on the square of coefficient magnitudes. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Review l2 regularization adds a penalty based on the square of coefficient magnitudes., then practise related AI-900 questions on the same topic to reinforce the concept.
Describe fundamental principles of machine learning on Azure — This question tests Describe fundamental principles of machine learning on Azure — L2 regularization adds a penalty based on the square of coefficient magnitudes..
What is the correct answer to this question?
The correct answer is: Apply L2 regularization to the model — 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.
What should I do if I get this AI-900 question wrong?
Review l2 regularization adds a penalty based on the square of coefficient magnitudes., then practise related AI-900 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
L2 regularization adds a penalty based on the square of coefficient magnitudes.
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