- A
A) Underfitting
Why wrong: Underfitting occurs when the model fails to capture patterns in the training data, resulting in poor performance on both training and test sets, which is not the case here.
- B
B) Overfitting
Overfitting happens when the model memorizes the training data, including noise, leading to high training accuracy but low test accuracy. This matches the described 99% training vs 70% test accuracy.
- C
C) Bias-variance tradeoff
Why wrong: Bias-variance tradeoff is a conceptual framework explaining the tension between underfitting and overfitting. It is not the direct term for the observed performance pattern.
- D
D) Regularization
Why wrong: Regularization is a technique used to reduce overfitting by penalizing complex models. It is a solution, not the problem described.
Quick Answer
The answer is overfitting. This is the correct choice because the model’s high training accuracy of 99% paired with a significantly lower test accuracy of 70% is the classic symptom of overfitting, where the model has memorized noise and specific patterns in the training data instead of learning generalizable features. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your understanding of model evaluation and the bias-variance tradeoff, often appearing in questions that contrast overfitting with underfitting or proper generalization. A common trap is to confuse overfitting with high variance or to assume high training accuracy always indicates a good model. Remember the memory tip: “If it aces the homework but flunks the final, it’s overfitting.”
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. 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.
A data scientist trains a classification model on a dataset of 10,000 labeled emails to distinguish spam from non-spam. The model achieves 99% accuracy on the training data but only 70% accuracy on a held-out test set. Which term best describes this situation?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
B) Overfitting
The model performs exceptionally well on training data (99% accuracy) but poorly on unseen test data (70% accuracy), which is the classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training set rather than generalizing to new data, often due to excessive model complexity or insufficient regularization.
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.
- ✗
A) Underfitting
Why it's wrong here
Underfitting occurs when the model fails to capture patterns in the training data, resulting in poor performance on both training and test sets, which is not the case here.
- ✓
B) Overfitting
Why this is correct
Overfitting happens when the model memorizes the training data, including noise, leading to high training accuracy but low test accuracy. This matches the described 99% training vs 70% test accuracy.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
C) Bias-variance tradeoff
Why it's wrong here
Bias-variance tradeoff is a conceptual framework explaining the tension between underfitting and overfitting. It is not the direct term for the observed performance pattern.
- ✗
D) Regularization
Why it's wrong here
Regularization is a technique used to reduce overfitting by penalizing complex models. It is a solution, not the problem described.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'high training accuracy' with a good model, failing to recognize that the large gap between training and test performance is the hallmark of overfitting, not underfitting or a tradeoff concept.
Detailed technical explanation
How to think about this question
Overfitting often arises when the model has too many parameters relative to the number of training samples, such as a deep neural network with thousands of features on only 10,000 emails. In Azure Machine Learning, you can detect overfitting by monitoring the gap between training and validation metrics during automated ML runs, and mitigate it using techniques like early stopping, dropout, or reducing model complexity. A real-world scenario is spam filtering where an overfitted model might flag emails containing specific rare words from the training set while missing common spam patterns.
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.
What to study next
Got this wrong? Here's your next step.
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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: B) Overfitting — The model performs exceptionally well on training data (99% accuracy) but poorly on unseen test data (70% accuracy), which is the classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training set rather than generalizing to new data, often due to excessive model complexity or insufficient regularization.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 →
Same concept, more angles
2 more ways 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. A data scientist is training a classification model on a dataset with 100 features and only 500 labeled samples. The model achieves 99% accuracy on the training data but only 68% accuracy on a held-out test set, indicating overfitting. Which technique is most appropriate to directly address this problem?
medium- A.Increase the amount of training data by collecting more samples
- ✓ B.Reduce the number of features used for training
- C.Increase the complexity of the model by adding more layers
- D.Train for more epochs
Why B: Option B is correct because reducing the number of features directly combats overfitting by decreasing model complexity and the risk of learning noise from irrelevant or redundant features. With only 500 samples and 100 features, the model has a high variance problem; feature selection or dimensionality reduction (e.g., using Azure Machine Learning's Filter-Based Feature Selection or PCA) simplifies the hypothesis space, improving generalization to the test set.
Variation 2. A data scientist is developing a classification model to detect fraudulent transactions. The dataset is split into training and test sets. The data scientist repeatedly tunes the model's hyperparameters and evaluates performance on the test set until the test accuracy reaches 95%. However, when the model is deployed on new, unseen data, its accuracy drops to 70%. Which concept best explains this performance degradation?
medium- A.Overfitting to the training data
- B.Data leakage from the training set to the test set
- ✓ C.Overfitting to the test set
- D.Underfitting the training data
Why C: Option C is correct because the data scientist repeatedly tuned hyperparameters based on test set performance, effectively using the test set as part of the training process. This causes the model to become specialized to the test set's specific patterns and noise, so it fails to generalize to new, unseen data. This phenomenon is known as overfitting to the test set, where the test set no longer provides an unbiased estimate of real-world performance.
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Last reviewed: Jun 11, 2026
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