- A
When a model is trained on too much data and becomes too accurate
Why wrong: More data generally reduces overfitting rather than causing it — overfitting is about memorising noise in existing data.
- B
When a model learns training data too specifically and fails to generalise to new data
Overfitting means high training accuracy but poor test accuracy — the model memorised noise instead of learning general patterns.
- C
When a model's predictions exceed the acceptable numerical range
Why wrong: Numerical range issues relate to output scaling — overfitting is about poor generalisation from training to unseen data.
- D
When Azure ML runs training for longer than the allocated compute budget
Why wrong: Compute budget overruns are a resource issue — overfitting is a model quality problem related to generalisation.
What Is Overfitting and How Does Azure ML Help Prevent It?
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 'overfitting' in machine learning and how does Azure ML help prevent it?
Quick Answer
The correct answer is that overfitting happens when a model learns the training data too specifically and fails to generalise to new data. This occurs because the model memorizes noise, outliers, and irrelevant patterns in the training set rather than capturing the underlying trend, leading to high accuracy on training data but poor performance on unseen examples. On the AI-900 exam, this concept tests your understanding of model evaluation and generalization, often appearing in questions about training/validation splits or AutoML features. A common trap is confusing overfitting with underfitting—remember that overfitting is like a student who memorizes answers to a practice test but cannot solve a slightly different problem. Azure ML helps prevent overfitting through automated machine learning (AutoML), which applies regularization, cross-validation, and early stopping, and also simplifies configuring train/test splits and hyperparameter tuning. Memory tip: think “overfitting = over-memorizing, not generalizing.”
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 learns training data too specifically and fails to generalise to new data
Overfitting occurs when a machine learning model learns the training data too precisely, including noise and outliers, resulting in poor performance on unseen data. Azure ML helps prevent overfitting through automated machine learning (AutoML) which applies regularization, cross-validation, and early stopping techniques, as well as by enabling easy configuration of train/test splits and hyperparameter tuning.
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.
- ✗
When a model is trained on too much data and becomes too accurate
Why it's wrong here
More data generally reduces overfitting rather than causing it — overfitting is about memorising noise in existing data.
- ✓
When a model learns training data too specifically and fails to generalise to new data
Why this is correct
Overfitting means high training accuracy but poor test accuracy — the model memorised noise instead of learning general patterns.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
When a model's predictions exceed the acceptable numerical range
Why it's wrong here
Numerical range issues relate to output scaling — overfitting is about poor generalisation from training to unseen data.
- ✗
When Azure ML runs training for longer than the allocated compute budget
Why it's wrong here
Compute budget overruns are a resource issue — overfitting is a model quality problem related to generalisation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse overfitting with high accuracy or large datasets, but the key is that overfitting is about poor generalization, not just high performance on training data.
Trap categories for this question
Command / output trap
Numerical range issues relate to output scaling — overfitting is about poor generalisation from training to unseen data.
Detailed technical explanation
How to think about this question
Under the hood, overfitting is often detected by monitoring the divergence between training and validation loss curves. Azure ML's automated machine learning uses techniques like L1/L2 regularization (adding a penalty to the loss function) and early stopping (halting training when validation performance stops improving) to mitigate overfitting. In a real-world scenario, a model trained on customer churn data might memorize specific customer IDs instead of learning general patterns, leading to poor predictions on new customers.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: When a model learns training data too specifically and fails to generalise to new data — Overfitting occurs when a machine learning model learns the training data too precisely, including noise and outliers, resulting in poor performance on unseen data. Azure ML helps prevent overfitting through automated machine learning (AutoML) which applies regularization, cross-validation, and early stopping techniques, as well as by enabling easy configuration of train/test splits and hyperparameter tuning.
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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