AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A data scientist is training a regression model to predict energy consumption. The dataset includes features like temperature, humidity, time of day, and day of week. After training, the model performs well on the training set but poorly on new data. Which approach would most likely help reduce this problem?
⚠ Common exam trap
Watch out — candidates often confuse 'poor performance on new data' with underfitting and incorrectly choose to add more features or increase complexity, when the symptom of high training accuracy with low test accuracy clearly indicates overfitting requiring simplification.
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 simpler model with fewer parameters.
The model performs well on the training set but poorly on new data, which is classic overfitting. Using a simpler model with fewer parameters reduces the model's capacity to memorize noise and irrelevant patterns, forcing it to learn the underlying generalizable relationships. This directly addresses the variance problem without requiring additional data or computational resources.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add more features to the model.
Why it's wrong here
Adding more features to the regression model increases the dimensionality of the feature space, which gives the model additional degrees of freedom to fit idiosyncratic noise in the training set rather than the true underlying relationship. This tends to raise variance, can introduce multicollinearity, and is the opposite of the bias-reduction usually needed. It should be paired with regularization or feature selection if used at all.
- ✓
Use a simpler model with fewer parameters.
Why this is correct
Switching to a simpler regression model, such as a linear model instead of a high-degree polynomial, reduces the number of learnable parameters and therefore limits the model's capacity to memorize noise. This pushes the model toward the bias end of the bias-variance tradeoff, lowering variance and improving out-of-sample generalization. It is the most direct way to address overfitting when training data is limited.
- ✗
Increase the number of training epochs.
Why it's wrong here
Training epochs control how many passes the optimizer makes over the data; more epochs allow the gradient-descent updates to keep minimizing the training loss, which often means fitting the training set even more exactly. If the model is already overfitting, additional epochs will only sharpen that noise-fitting behavior, especially in the absence of early stopping or regularization. Early stopping would imply fewer, not more, epochs.
- ✗
Use a more complex model to capture more patterns.
Why it's wrong here
Choosing a more complex architecture or adding interaction terms and nonlinearities increases the hypothesis space and lets the regression function bend around every data point, including outliers and random error. With finite samples this increases variance and makes overfitting worse rather than resolving it. The correct move would be to reduce complexity or add regularization, not escalate it.
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Overfitting
Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, causing it to perform poorly on new, unseen data.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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