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

Question 1

A data scientist is training a regression model to predict house prices. The model performs near perfectly on the training data but poorly on a held-out test set. The scientist suspects the model is memorizing the training data instead of learning general patterns. Which technique is most appropriate to directly address this issue?

Accepted Answer

Apply L2 regularization to the model. L2 regularization (also known as Ridge regularization) directly addresses overfitting by adding a penalty term proportional to the square of the model weights to the loss function. This discourages the model from assigning excessively large coefficients to features, forcing it to learn simpler, more general patterns rather than memorizing noise in the training data.

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Increase the size of the training dataset

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Increase the complexity of the model (e.g., add more features)

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Switch to a different regression algorithm

Question 2

A data scientist trains a regression model to predict house prices using features like square footage, number of bedrooms, and location. The model achieves very high accuracy on the training data but performs poorly on a held-out test set. Which technique should the data scientist apply to reduce overfitting?

Accepted Answer

Use regularization. Regularization (Option C) is the correct technique to reduce overfitting because it adds a penalty term to the loss function (e.g., L1 or L2 regularization), which discourages the model from learning overly complex patterns that fit noise in the training data. This helps the model generalize better to unseen data, such as the held-out test set, by constraining the magnitude of feature weights.

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Increase the number of features

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Decrease the training data size

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Increase the number of training epochs

Question 3

A botanist uses Azure Automated Machine Learning to train a model that classifies iris flowers into three species: setosa, versicolor, and virginica. The dataset contains exactly 50 examples of each species, making it perfectly balanced. The botanist wants the primary metric to give equal importance to the classification performance of each species, regardless of their frequency. Which primary metric should the botanist select in Azure AutoML?

Accepted Answer

Normalized macro recall. Azure AutoML does not support Macro F1 as a primary classification metric. The supported primary metric that gives equal importance to each class is normalized macro recall (norm_macro_recall). It averages recall across classes after normalizing by class frequency, so each species contributes equally even if class sizes differ.

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Accuracy

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Weighted F1

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Micro F1

Question 4

A data scientist trains a model to predict customer churn. The dataset includes features like age, income, and number of support calls. The model performs well on historical data but poorly on new data from a different customer segment. Which technique is most likely to help improve generalization?

Accepted Answer

Feature engineering. The model overfits to the historical customer segment. Cross-validation (B) merely validates on subsets of the same data and cannot improve generalization to a different distribution. Feature engineering (A) is more appropriate because it allows creation of features that capture generalizable patterns (e.g., normalized income or segment-specific interactions) that help the model perform on new segments. Increasing model complexity (C) or larger learning rate (D) would worsen overfitting and generalization.

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Cross-validation

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Increasing model complexity

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Using a larger learning rate

Question 5

A data scientist trains a binary classification model to predict whether a loan applicant will default (positive class) or not (negative class). The training data contains 5% default cases. The model predicts 'no default' for every applicant in the test set and achieves 95% accuracy. Which evaluation metric best reveals that the model is failing to identify any default cases?

Accepted Answer

B. Recall for the default class. Recall for the default class (positive class) measures the proportion of actual default cases that the model correctly identifies. With a model that predicts 'no default' for every applicant, recall for the default class is 0% because it fails to identify any true positive cases. This metric directly reveals the model's inability to detect defaults, despite the high overall accuracy of 95%.

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A. Precision for the default class

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C. F1-score for the default class

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D. Overall accuracy

Question 6

A data scientist trains a multiclass classification model to categorize customer support tickets into three types: 'Billing', 'Technical', and 'General'. The dataset contains 80% 'General', 15% 'Billing', and only 5% 'Technical' tickets. Overall accuracy on a test set is 85%, but the model misclassifies most 'Technical' tickets as 'General'. Which metric would best help the data scientist understand the model's poor performance on the 'Technical' class?

Accepted Answer

F1-score for the 'Technical' class. The F1-score for the 'Technical' class is the best metric because it combines precision and recall into a single harmonic mean, directly capturing the model's inability to correctly identify the minority class. Overall accuracy is misleading on imbalanced datasets; for example, a model that always predicted 'General' would achieve 80% accuracy, and while 85% is slightly higher, it still masks the model's poor performance on the minority 'Technical' class. The F1-score penalizes both false positives and false negatives, making it a standard metric for evaluating classifier performance on imbalanced classes.

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Overall accuracy

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Confusion matrix

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Precision for the 'General' class

Question 7

A data scientist trains a regression model to predict house prices using features like bedrooms, square footage, and location. The model achieves an R-squared of 0.95 on the test set. However, when deployed to predict prices in a new city with different property characteristics, the predictions are very inaccurate. Which concept best explains this poor performance?

Accepted Answer

Overfitting. The model achieved an R-squared of 0.95 on the test set, indicating excellent performance on data from the same distribution. However, when deployed to a new city with different property characteristics, the predictions were very inaccurate. This is a classic symptom of overfitting, where the model has learned noise and patterns specific to the training data (e.g., city-specific price trends) that do not generalize to unseen data from a different distribution.

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Underfitting

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High bias

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Data drift

Question 8

What is the difference between 'supervised' and 'unsupervised' learning?

Accepted Answer

Supervised learning trains on labelled data; unsupervised discovers patterns in unlabelled data. Supervised learning requires a labeled dataset where each training example has an input-output pair, allowing the model to learn a mapping from inputs to known outputs. In contrast, unsupervised learning works with unlabeled data, and the model must find inherent patterns, groupings, or structures (e.g., clustering or dimensionality reduction) without any predefined labels. This distinction is fundamental to choosing the right machine learning approach in Azure, such as using Azure Machine Learning for supervised tasks like regression/classification and unsupervised tasks like clustering.

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Supervised learning requires a human to watch the training process; unsupervised runs automatically

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Supervised learning uses neural networks; unsupervised uses decision trees only

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Unsupervised learning always produces better results than supervised learning

Question 9

What is the 'bias-variance tradeoff' in machine learning?

Accepted Answer

The tradeoff between underfitting (high bias) and overfitting (high variance) when choosing model complexity. The bias-variance tradeoff describes the inverse relationship between underfitting (high bias, where the model is too simple to capture patterns) and overfitting (high variance, where the model is too complex and captures noise). In Azure Machine Learning, this tradeoff is managed by tuning hyperparameters like regularization strength or tree depth to balance model complexity and generalization.

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The tradeoff between model accuracy and inference speed

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The tradeoff between training data quantity and model quality

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The difference in fairness metrics between biased and unbiased model versions

Question 10

Match each Azure AI service to its pricing model.

Accepted Answer

Azure Cognitive Services: Pay-as-you-go per transaction. Azure AI services generally use pay-as-you-go or consumption-based pricing. Cognitive Services charge per transaction, Azure Machine Learning charges for compute and workspace, Bot Service charges per message, and Cognitive Search charges per index and queries. Be careful not to confuse these models.

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Azure Cognitive Services: Flat monthly fee

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Azure Machine Learning: Consumption-based per message

Describe fundamental principles of machine learning on Azure

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