- A
Increase the amount of training data by collecting more samples
Why wrong: More data generally helps reduce overfitting, but this option is about 'collecting more samples' which is not always possible and is not a technique applied to the current dataset. The question asks for a direct technique to address overfitting given the existing setup.
- B
Reduce the number of features used for training
Reducing the number of features (e.g., via feature selection or PCA) decreases model complexity, making it less likely to overfit. This is a standard regularization technique especially useful when features outnumber samples.
- C
Increase the complexity of the model by adding more layers
Why wrong: Increasing model complexity (e.g., more layers or neurons) would make overfitting worse because the model becomes more capable of memorizing training data.
- D
Train for more epochs
Why wrong: Training for more epochs typically increases the risk of overfitting, as the model will continue to learn noise in the training data. Early stopping is a technique to prevent this, but simply increasing epochs is not appropriate.
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.
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?
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 used for training
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.
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 amount of training data by collecting more samples
Why it's wrong here
More data generally helps reduce overfitting, but this option is about 'collecting more samples' which is not always possible and is not a technique applied to the current dataset. The question asks for a direct technique to address overfitting given the existing setup.
- ✓
Reduce the number of features used for training
Why this is correct
Reducing the number of features (e.g., via feature selection or PCA) decreases model complexity, making it less likely to overfit. This is a standard regularization technique especially useful when features outnumber samples.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the complexity of the model by adding more layers
Why it's wrong here
Increasing model complexity (e.g., more layers or neurons) would make overfitting worse because the model becomes more capable of memorizing training data.
- ✗
Train for more epochs
Why it's wrong here
Training for more epochs typically increases the risk of overfitting, as the model will continue to learn noise in the training data. Early stopping is a technique to prevent this, but simply increasing epochs is not appropriate.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume more data (Option A) is always the best fix for overfitting, but the question explicitly tests the ability to choose a technique that directly addresses the high-dimensional, low-sample scenario without requiring additional data collection.
Detailed technical explanation
How to think about this question
Overfitting occurs when a model learns the training data's noise instead of the underlying signal, often quantified by a large gap between training and test error. In Azure Machine Learning, techniques like L1 regularization (Lasso) can automatically perform feature selection by shrinking irrelevant feature coefficients to zero, which is particularly effective when the number of features exceeds the number of samples. A real-world scenario is medical diagnosis with high-dimensional genomic data (e.g., 20,000 genes, 200 patients), where feature reduction is critical to avoid spurious correlations.
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: Reduce the number of features used for training — 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.
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.
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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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