The correct answer is to apply regularization. This is because the model’s perfect 100% training accuracy paired with only 60% validation accuracy is a textbook case of overfitting, where the network has memorized noise and specific patterns in the small dataset instead of learning generalizable features. Regularization techniques, such as L1/L2 weight penalties or dropout, constrain the model’s complexity by penalizing large weights or randomly disabling neurons during training, forcing the network to rely on simpler, more robust representations. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your understanding of overfitting regularization as a core machine learning concept, often appearing in questions about deep learning on limited data. A common trap is to assume more training epochs or a larger model will help, but that worsens overfitting. Remember the memory tip: “Perfect train, poor test? Regularize to rest.”
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 deep neural network on a small dataset. The model achieves 100% accuracy on the training data but only 60% accuracy on a validation set. Which technique is most appropriate to address this issue?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply regularization
The model's perfect training accuracy (100%) paired with poor validation accuracy (60%) is a classic sign of overfitting, where the model has memorized the training data rather than learning generalizable patterns. Regularization techniques (e.g., L1/L2 regularization, dropout) penalize large weights or randomly drop neurons during training, which forces the network to learn simpler, more robust features and reduces overfitting on small datasets.
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
Why it's wrong here
Increasing epochs would likely worsen overfitting by allowing the model to memorize more training details.
✗
Add more hidden layers
Why it's wrong here
Adding layers increases model complexity, which typically exacerbates overfitting.
✓
Apply regularization
Why this is correct
Regularization adds constraints to the model to prevent overfitting by discouraging overly complex patterns.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the learning rate
Why it's wrong here
Increasing the learning rate can cause instability and may not address overfitting; it might even hurt performance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse overfitting with underfitting and incorrectly choose options that increase model complexity (more layers or epochs) or speed up training (higher learning rate), rather than recognizing that regularization is the standard technique to combat overfitting.
Detailed technical explanation
How to think about this question
Regularization works by adding a penalty term to the loss function (e.g., L2 regularization adds λ * Σ(w²)), which constrains weight magnitudes and prevents the network from fitting noise. In deep learning, dropout regularization randomly sets a fraction of neuron activations to zero during each forward pass (e.g., 0.5 for hidden layers), effectively training an ensemble of sub-networks and improving generalization. For small datasets, early stopping (monitoring validation loss) is also commonly combined with regularization to halt training before overfitting occurs.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
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 — The model's perfect training accuracy (100%) paired with poor validation accuracy (60%) is a classic sign of overfitting, where the model has memorized the training data rather than learning generalizable patterns. Regularization techniques (e.g., L1/L2 regularization, dropout) penalize large weights or randomly drop neurons during training, which forces the network to learn simpler, more robust features and reduces overfitting on small datasets.
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.
About these practice questions
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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 trains a machine learning model on a dataset of housing prices. The model achieves 98% accuracy on the training data but only 72% accuracy on a separate test set. What is the most likely problem with this model?
medium
A.Underfitting
✓ B.Overfitting
C.Data leakage
D.Class imbalance
Why B: The model's high accuracy on training data (98%) but significantly lower accuracy on test data (72%) is a classic symptom of overfitting, where the model learns noise and specific patterns in the training set rather than generalizing to new, unseen data. In Azure Machine Learning, this often occurs when the model is too complex (e.g., deep decision trees or high-degree polynomial features) relative to the amount of training data, and regularization techniques like L1/L2 regularization or early stopping are not applied.
Variation 2. A data scientist trains a machine learning model on historical sales data to predict future sales volume. The model achieves 99% accuracy on the training dataset but only 75% accuracy on a separate test dataset. What is the most likely issue with this model?
medium
A.Underfitting
✓ B.Overfitting
C.High bias
D.High variance
Why B: The model's 99% accuracy on the training set versus 75% on the test set indicates it has memorized the training data, including noise and outliers, rather than learning generalizable patterns. This classic symptom of overfitting occurs when the model is too complex relative to the amount or variability of the training data, causing poor performance on unseen data.
Variation 3. A data scientist trains a machine learning model to predict house prices based on features like square footage, number of bedrooms, and location. The model achieves a very low error on the training data but performs poorly on a held-out test set. Which term best describes this situation?
medium
A.Underfitting
✓ B.Overfitting
C.High bias
D.High variance
Why B: The model performs exceptionally well on training data but poorly on test data, 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 unseen data. In Azure Machine Learning, this can be detected by monitoring the gap between training and validation metrics, and mitigated using techniques like regularization or early stopping.
Variation 4. 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?
medium
A.Reduce the amount of training data
✓ B.Apply regularization to the model
C.Remove some features from the dataset
D.Increase the number of layers in the neural network
Why B: 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.
Last reviewed: Jun 11, 2026
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