- A
Increase the complexity of the model by adding more layers or parameters
Why wrong: Increasing model complexity would typically worsen overfitting, as the model would have more capacity to memorize the training data.
- B
Add more training data
Why wrong: Adding more data can reduce overfitting, but it is not always feasible. Reducing features is often a more practical first step. However, this option is not the most likely among the given choices because reducing features directly addresses the high dimensionality.
- C
Reduce the number of features or apply regularization
Reducing features simplifies the model, making it less prone to overfitting. Regularization also penalizes large coefficients. This is a direct and effective method to improve generalization.
- D
Use a different train-test split ratio like 80-20 instead of 70-30
Why wrong: Changing the split ratio slightly may affect evaluation but does not address the underlying overfitting problem. The model architecture remains unchanged.
Quick Answer
The answer is to reduce the number of features or apply regularization. This is correct because the model shows classic signs of overfitting—low training error paired with high test error—indicating high variance where the model has memorized noise rather than learning general patterns. Regularization techniques like L1 (Lasso) or L2 (Ridge) penalize large coefficients, effectively constraining model complexity and forcing it to improve generalization overfitting regression scenarios. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of bias-variance tradeoff and model tuning, often appearing in questions about diagnosing overfitting and selecting mitigation strategies. A common trap is assuming more data alone fixes overfitting, but without reducing complexity, the model may still memorize. Remember the memory tip: “High variance? Constrain the variance—regularize or reduce features.”
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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: overfitting occurs when a model learns the training data too well, including noise.. 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 regression model on a dataset with 100 features and 10,000 samples. The model achieves a low training error but a much higher error on a held-out test set. Which approach is most likely to improve the model's generalization performance?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
Reduce the number of features or apply regularization
The model exhibits high variance (overfitting), as indicated by low training error but high test error. Reducing the number of features or applying regularization (e.g., L1/L2) directly constrains model complexity, forcing it to learn more general patterns rather than memorizing noise. This is the standard approach to improve generalization in regression models.
Key principle: Overfitting occurs when a model learns the training data too well, including noise.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the complexity of the model by adding more layers or parameters
Why it's wrong here
Increasing model complexity would typically worsen overfitting, as the model would have more capacity to memorize the training data.
- ✗
Add more training data
Why it's wrong here
Adding more data can reduce overfitting, but it is not always feasible. Reducing features is often a more practical first step. However, this option is not the most likely among the given choices because reducing features directly addresses the high dimensionality.
- ✓
Reduce the number of features or apply regularization
Why this is correct
Reducing features simplifies the model, making it less prone to overfitting. Regularization also penalizes large coefficients. This is a direct and effective method to improve generalization.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Overfitting occurs when a model learns the training data too well, including noise.
- ✗
Use a different train-test split ratio like 80-20 instead of 70-30
Why it's wrong here
Changing the split ratio slightly may affect evaluation but does not address the underlying overfitting problem. The model architecture remains unchanged.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume adding more training data is always the best fix for overfitting, but the question specifically describes a model with 100 features and only 10,000 samples—feature reduction or regularization is the more direct and efficient solution.
Detailed technical explanation
How to think about this question
Regularization techniques like Lasso (L1) or Ridge (L2) add a penalty term to the loss function, effectively shrinking feature coefficients toward zero. L1 regularization can drive some coefficients to exactly zero, performing automatic feature selection—critical when many features are irrelevant or noisy. In Azure Machine Learning, you can implement this via the 'Lasso' or 'Ridge' regression modules, or by using the 'Regularization Strength' hyperparameter in automated ML.
KKey Concepts to Remember
- Overfitting occurs when a model learns the training data too well, including noise.
- Regularization penalizes large model coefficients to prevent overfitting.
- Feature reduction simplifies the model by removing irrelevant or redundant features.
- High dimensionality (many features) increases the risk of overfitting.
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
Overfitting occurs when a model learns the training data too well, including noise.
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. Overfitting occurs when a model learns the training data too well, including noise. 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.
Review overfitting occurs when a model learns the training data too well, including noise., then practise related AI-900 questions on the same topic to reinforce the concept.
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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 — Overfitting occurs when a model learns the training data too well, including noise..
What is the correct answer to this question?
The correct answer is: Reduce the number of features or apply regularization — The model exhibits high variance (overfitting), as indicated by low training error but high test error. Reducing the number of features or applying regularization (e.g., L1/L2) directly constrains model complexity, forcing it to learn more general patterns rather than memorizing noise. This is the standard approach to improve generalization in regression models.
What should I do if I get this AI-900 question wrong?
Review overfitting occurs when a model learns the training data too well, including noise., then practise related AI-900 questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Overfitting occurs when a model learns the training data too well, including noise.
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Last reviewed: Jun 11, 2026
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.
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