What Is Overfitting in Machine Learning?
What is overfitting in machine learning?
Quick Answer
The correct answer is that overfitting in machine learning occurs when a model performs well on training data but poorly on new, unseen data. This happens because the model has learned the training data too precisely, capturing noise and outliers as if they were meaningful patterns, which destroys its ability to generalize. For the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of the core goal of machine learning: building models that work on real-world data, not just the dataset used for training. A common trap is confusing high training accuracy with a good model—overfitting actually signals poor performance. Azure Machine Learning addresses this with techniques like regularization, cross-validation, and early stopping. To remember it, think of a student who memorizes the exact answers to practice questions but fails the real exam because they never learned the underlying principles.
⚠ Common exam trap
Watch out — candidates often confuse overfitting with underfitting (Option D) or mistakenly think overfitting is caused solely by insufficient data (Option B), when in fact overfitting is about the model's inability to generalize due to excessive complexity or noise memorization.
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
✓
When a model performs well on training data but poorly on new, unseen data
Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, resulting in high accuracy on training data but poor generalization to new, unseen data. This is a fundamental concept in ML because the goal is to create models that perform well on real-world data, not just the data they were trained on. In Azure Machine Learning, techniques like regularization, cross-validation, and early stopping are used to detect and mitigate overfitting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
When a model performs well on training data but poorly on new, unseen data
Why this is correct
Overfitting occurs when a model memorises training data, including noise, so training performance is high while generalisation to new, unseen data is poor. This gap between training and validation performance is the defining characteristic of the condition.
- ✗
When a model is trained with too little data
Why it's wrong here
Training with too little data causes underfitting, where the model fails to capture the underlying pattern and performs poorly on both training and new data. It is tempting because both are data-quantity problems, and it would be correct if the stem asked about a model that cannot generalise due to insufficient examples.
- ✗
When a model takes too long to train
Why it's wrong here
Overfitting concerns a model memorising training data so it generalises poorly to unseen data; training duration is unrelated. It tempts because lengthy training often accompanies complex models, but the defining symptom is strong training performance paired with weak validation performance.
- ✗
When a model performs poorly on both training and test data
Why it's wrong here
Underfitting, not overfitting, describes poor performance on both training and test data; overfitting means low training error with high test error, because the model memorised noise. It is tempting because a model failing everywhere genuinely signals insufficient capacity or training, which is the scenario where adding features or training longer would be the correct fix.
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Custom Vision Models: Training and Evaluation
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
Azure Machine Learning
Azure Machine Learning is a cloud service for building, training, and deploying machine learning models at scale.
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Same concept, more angles
1 more way 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. What is 'regularization' in machine learning and why is it used?
medium- A.Normalizing input data to a standard scale before training
- ✓ B.Adding a complexity penalty to the training objective to reduce overfitting
- C.Ensuring models comply with AI regulations in different jurisdictions
- D.Standardizing the format of training data from different sources
Why B: Regularization is a technique used to reduce overfitting by adding a penalty term to the loss function during training. This penalty discourages the model from learning overly complex patterns (e.g., large weights) that fit the training data too closely but fail to generalize to new data. In Azure Machine Learning, regularization can be applied via algorithms like Lasso (L1) or Ridge (L2) regression, which directly modify the optimization objective.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.