AI-900 Practice Question: Describe fundamental principles of machine learning on Azure

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 by adding more layers or parameters gives the model greater capacity to fit the training data almost perfectly, which amplifies overfitting. With a small dataset of 100 samples, an overly complex model will memorize idiosyncrasies and random noise, driving test error even higher. This is the opposite of the needed intervention and would worsen generalization rather than improve it.

  When this WOULD be correct

  If the model had high training error and high test error (underfitting), increasing complexity (e.g., adding layers or parameters) would help capture patterns better.

• ✗

  Add more training data

  Why it's wrong here

  Adding more training data is unlikely to resolve the high test error here because the model already exhibits overfitting—low training error paired with high test error—indicating it has memorised noise rather than learned generalisable patterns. This approach is tempting because additional data often reduces variance in underfitting scenarios where the model lacks sufficient examples to capture underlying trends. However, in this case, the core issue is excessive model complexity relative to the dataset, which requires regularisation or feature reduction, not merely more samples.

  When this WOULD be correct

  In a scenario where the model has high training and test error (underfitting) due to insufficient data, adding more training data would improve generalization by providing more representative samples.

• ✓

  Reduce the number of features or apply regularization

  Why this is correct

  Reducing the number of features or applying regularization (e.g., L1/L2) directly targets overfitting by simplifying the model and penalizing large coefficients. This forces the model to focus on generalizable patterns instead of memorizing noise in the training set, which improves test performance. With only 100 samples, regularization is especially effective because it stabilizes coefficient estimates when the feature count is relatively high.

• ✗

  Use a different train-test split ratio like 80-20 instead of 70-30

  Why it's wrong here

  Changing the train-test split ratio, such as moving from 70-30 to 80-20, only alters the proportion of data used for training versus validation, not the model's underlying architecture or complexity. Since the model remains equally over-parameterized, it will still overfit the training data, and any change in test error arises from different random sampling of a small dataset, not from fixing the variance problem. This approach does not address the root cause of overfitting.

  When this WOULD be correct

  A question where the model has high variance due to a small training set, and the test set is too small to reliably estimate performance. In that case, increasing the training set size (e.g., from 70% to 80%) could reduce variance and improve generalization, especially if the model is complex.

Option-by-option analysis

Why each answer is right or wrong

Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.

Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”

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