- A
Reduce the amount of training data
Why wrong: Reducing training data would likely worsen generalization by providing less information to learn from. It does not address overfitting.
- B
Apply regularization to the model
Regularization (e.g., L1 or L2) discourages overly complex models by penalizing large coefficients, which helps reduce overfitting and improves performance on unseen data.
- C
Remove some features from the dataset
Why wrong: Removing features may help if irrelevant features cause noise, but it is not as direct or systematic as regularization. It might also remove useful information.
- D
Increase the number of layers in the neural network
Why wrong: Increasing model complexity typically worsens overfitting. Adding layers would likely make the model even more prone to memorizing training data.
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 model to predict house prices. The model achieves 99% accuracy on the training data but only 80% accuracy on new test data. Which technique is most likely to help improve the model's generalization?
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
Apply regularization to the model
The model is overfitting: it has memorized the training data (99% accuracy) but fails to generalize to new data (80% accuracy). Regularization (e.g., L1 or L2) penalizes large weights, reducing the model's complexity and forcing it to learn simpler patterns that generalize better. This directly addresses the variance problem without discarding useful information.
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.
- ✗
Reduce the amount of training data
Why it's wrong here
Reducing training data would likely worsen generalization by providing less information to learn from. It does not address overfitting.
- ✓
Apply regularization to the model
Why this is correct
Regularization (e.g., L1 or L2) discourages overly complex models by penalizing large coefficients, which helps reduce overfitting and improves performance on unseen data.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Remove some features from the dataset
Why it's wrong here
Removing features may help if irrelevant features cause noise, but it is not as direct or systematic as regularization. It might also remove useful information.
- ✗
Increase the number of layers in the neural network
Why it's wrong here
Increasing model complexity typically worsens overfitting. Adding layers would likely make the model even more prone to memorizing training data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse overfitting with underfitting and choose to increase model complexity (Option D) or reduce data (Option A), when the correct response is to simplify the model via regularization.
Detailed technical explanation
How to think about this question
Regularization works by adding a penalty term to the loss function — L2 (Ridge) adds the sum of squared weights, L1 (Lasso) adds the sum of absolute weights, effectively shrinking coefficients toward zero. In Azure Machine Learning, you can apply regularization via hyperparameter tuning (e.g., using `RegularizationRate` in a linear learner) or by using built-in regularized models like Lasso or ElasticNet. A subtle behavior: L1 regularization can drive some weights to exactly zero, performing automatic feature selection, which is especially useful when you have many irrelevant features.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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: Apply regularization to the model — The model is overfitting: it has memorized the training data (99% accuracy) but fails to generalize to new data (80% accuracy). Regularization (e.g., L1 or L2) penalizes large weights, reducing the model's complexity and forcing it to learn simpler patterns that generalize better. This directly addresses the variance problem without discarding useful information.
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: "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?
Read the scenario before looking for a memorised answer.
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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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