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.
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.
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.
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 performs well on training data but poorly on new, unseen data
Why this is correct
Overfitting means the model memorized training data specifics (including noise) and fails to generalize to new examples.
Related concept
Read the scenario before looking for a memorised answer.
✗
When a model is trained with too little data
Why it's wrong here
Too little data can cause underfitting or overfitting — overfitting is specifically about the model being too complex for the data.
✗
When a model takes too long to train
Why it's wrong here
Training time is a performance concern — overfitting is about generalization, not training duration.
✗
When a model performs poorly on both training and test data
Why it's wrong here
Poor performance on both training and test data describes underfitting — overfitting is good on training, poor on test.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Overfitting is often caused by models with too many parameters relative to the number of training samples, such as deep neural networks or high-degree polynomial regression. In practice, Azure ML's automated machine learning (AutoML) uses techniques like L1/L2 regularization and cross-validation to penalize overly complex models. A real-world scenario is a fraud detection model that learns specific transaction IDs instead of general fraud patterns, causing it to fail on new fraud cases.
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 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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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.
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 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.
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 →
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.
Last reviewed: Jun 11, 2026
Question Discussion
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