- A
Increase the number of training epochs significantly.
Why wrong: More training epochs typically increase the risk of overfitting, as the model can memorize the training data more thoroughly. This would likely widen the accuracy gap.
- B
Apply regularization techniques such as L1 or L2 regularization.
Regularization adds a penalty for large weights, discouraging overly complex models. This helps reduce overfitting and improves performance on unseen data.
- C
Remove some of the training data to make the dataset smaller.
Why wrong: Reducing the amount of training data usually exacerbates overfitting because the model has fewer examples to learn from, making it more likely to memorize the remaining data.
- D
Add more layers and neurons to the neural network.
Why wrong: Increasing model complexity by adding more parameters generally increases the risk of overfitting, especially when the dataset is relatively small.
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. 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 to predict whether an email is 'phishing' or 'legitimate'. The model achieves 99% accuracy on the training data but only 68% accuracy on the test data. Which action is most likely to help 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
Apply regularization techniques such as L1 or L2 regularization.
The model's high training accuracy (99%) paired with much lower test accuracy (68%) is a classic sign of overfitting, where the model has memorized the training data rather than learning generalizable patterns. Regularization techniques like L1 (Lasso) or L2 (Ridge) add a penalty to the loss function that discourages overly complex models by shrinking the weights of less important features, directly reducing overfitting and improving generalization on unseen data.
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.
- ✗
Increase the number of training epochs significantly.
Why it's wrong here
More training epochs typically increase the risk of overfitting, as the model can memorize the training data more thoroughly. This would likely widen the accuracy gap.
- ✓
Apply regularization techniques such as L1 or L2 regularization.
Why this is correct
Regularization adds a penalty for large weights, discouraging overly complex models. This 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 of the training data to make the dataset smaller.
Why it's wrong here
Reducing the amount of training data usually exacerbates overfitting because the model has fewer examples to learn from, making it more likely to memorize the remaining data.
- ✗
Add more layers and neurons to the neural network.
Why it's wrong here
Increasing model complexity by adding more parameters generally increases the risk of overfitting, especially when the dataset is relatively small.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse high training accuracy with good model performance and incorrectly assume that more data or more complexity will fix the issue, when in fact the problem is overfitting and requires regularization or simpler models.
Detailed technical explanation
How to think about this question
Under the hood, L2 regularization adds a term λ * Σ(w²) to the loss function, penalizing large weight values and encouraging the model to use all features more evenly, which smooths decision boundaries. In contrast, L1 regularization adds λ * Σ|w|, which can drive some weights to zero, effectively performing feature selection. In Azure Machine Learning, these techniques can be applied via scikit-learn's LogisticRegression with penalty='l2' or using the 'regularization_rate' parameter in automated ML, and are especially critical when dealing with high-dimensional data like email text 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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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 techniques such as L1 or L2 regularization. — The model's high training accuracy (99%) paired with much lower test accuracy (68%) is a classic sign of overfitting, where the model has memorized the training data rather than learning generalizable patterns. Regularization techniques like L1 (Lasso) or L2 (Ridge) add a penalty to the loss function that discourages overly complex models by shrinking the weights of less important features, directly reducing overfitting and improving generalization on unseen data.
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.
About these practice questions
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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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