AI-900 Describe fundamental principles of machine learning on Azure Practice Test 3 — 15 Questions

Question 1

A manufacturing team wants to predict product defects based on sensor readings from the production line. They have 10,000 historical samples, each labeled as 'defective' or 'non-defective'. Which type of machine learning should they use in Azure Machine Learning?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Supervised learning. This is a supervised learning problem because the dataset contains labeled historical samples (defective or non-defective), and the goal is to predict a categorical outcome based on sensor readings. In Azure Machine Learning, supervised learning algorithms such as two-class logistic regression or boosted decision trees are used to train a model that maps input features to known labels.

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Unsupervised learning

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Reinforcement learning

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Semi-supervised learning

Question 2

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?

Accepted Answer

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.

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Increase the number of training epochs significantly.

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Remove some of the training data to make the dataset smaller.

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Add more layers and neurons to the neural network.

Question 3

A data scientist is building a machine learning model to predict whether a credit card transaction is fraudulent or legitimate. The dataset contains 100,000 historical transactions, each labeled as 'fraudulent' or 'legitimate'. Which type of machine learning task should the data scientist use in Azure Machine Learning?

Accepted Answer

Binary classification. Binary classification is the correct choice because the prediction task involves distinguishing between exactly two mutually exclusive classes: 'fraudulent' and 'legitimate'. In Azure Machine Learning, binary classification algorithms (e.g., Two-Class Logistic Regression, Two-Class Boosted Decision Tree) are designed to output a probability score for one of two labels, making them ideal for this fraud detection scenario.

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Regression

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Multi-class classification

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Clustering

Question 4

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?

Accepted Answer

Reduce the number of features used for training. 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.

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Increase the amount of training data by collecting more samples

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Increase the complexity of the model by adding more layers

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Train for more epochs

Question 5

A data scientist is building a classification model to detect fraudulent transactions. The dataset has 1,000,000 legitimate transactions and only 1,000 fraudulent ones. The model achieves 99.9% accuracy on the test set, but it fails to catch most fraudulent cases. Which metric should the data scientist prioritize to better evaluate the model's performance on this imbalanced dataset?

Accepted Answer

Recall. Recall measures the proportion of actual positive cases (fraudulent transactions) correctly identified by the model. With only 1,000 fraud cases out of 1,001,000 total transactions, a model that predicts 'legitimate' for every transaction would achieve 99.9% accuracy but 0% recall, making recall the critical metric for imbalanced fraud detection.

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Accuracy

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Mean Squared Error

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R-squared

Question 6

A data scientist trains a binary classification model to distinguish between images of cats and dogs. On the test set, the model achieves 98% accuracy, but a deeper inspection reveals that the test set contains 95% cats and 5% dogs, and the model predicts 'cat' for every single image. Which metric should the data scientist prioritize to get a more realistic evaluation of the model's performance on this imbalanced dataset?

Accepted Answer

F1-score. The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both when classes are imbalanced. In this scenario, accuracy is misleadingly high (98%) because the model always predicts the majority class (cat), achieving high accuracy without actually learning to distinguish cats from dogs. The F1-score penalizes the model for its poor recall on the minority class (dogs), giving a more realistic evaluation of its performance.

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Precision

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Recall

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Accuracy

Question 7

A retail company has a dataset of customer transaction records with no predefined categories. They want to identify natural groupings of customers based on their purchasing behavior to create targeted marketing campaigns. Which type of machine learning should they use in Azure Machine Learning?

Accepted Answer

Clustering. Clustering is the correct choice because the goal is to discover natural groupings in unlabeled data based on purchasing behavior. Azure Machine Learning provides clustering algorithms like K-Means that automatically partition customers into segments without predefined labels, enabling targeted marketing campaigns.

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Classification

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Regression

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Reinforcement learning

Question 8

A data scientist has a dataset containing thousands of labeled images of cats and dogs. The data scientist wants to train a model that can automatically classify new unlabeled images as either 'cat' or 'dog'. Which type of machine learning should the data scientist use?

Accepted Answer

Supervised learning. Supervised learning, because the dataset contains labeled images (each image is tagged as 'cat' or 'dog'), and the goal is to train a model to predict the label for new unlabeled images. Supervised learning algorithms, such as convolutional neural networks (CNNs), learn a mapping from input features (pixel values) to output labels using the provided ground-truth labels, enabling accurate classification on unseen data.

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Unsupervised learning

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Reinforcement learning

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Semi-supervised learning

Question 9

A data scientist is training a logistic regression model to predict customer churn using a small dataset with 500 records and 200 features. The model achieves 97% accuracy on the training set but only 65% on a held-out test set, indicating severe overfitting. The data scientist wants to reduce overfitting by automatically eliminating irrelevant features. Which technique should the data scientist apply?

Accepted Answer

Apply L1 regularization (Lasso) to the model. L1 regularization (Lasso) adds a penalty equal to the absolute value of the magnitude of coefficients, which can shrink some coefficients exactly to zero. This performs automatic feature selection by eliminating irrelevant features, directly addressing the overfitting caused by having 200 features on only 500 records. The high training accuracy (97%) versus low test accuracy (65%) is a classic sign of overfitting that L1 regularization mitigates by reducing model complexity.

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Apply L2 regularization (Ridge) to the model

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Use k-fold cross-validation to select the best model

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Increase the number of training samples by data augmentation

Question 10

A data scientist uses Azure Machine Learning to train a model that predicts the electricity consumption (in kilowatt-hours) of a building based on features like building age, square footage, and number of occupants. The data scientist wants to evaluate how accurately the model's predictions match the actual consumption values. Which evaluation metric is most appropriate for this regression task?

Accepted Answer

Mean Absolute Error (MAE). Mean Absolute Error (MAE) is the most appropriate metric for this regression task because it directly measures the average absolute difference between predicted and actual electricity consumption values. Unlike classification metrics, MAE provides an interpretable error in the same unit (kilowatt-hours) as the target variable, making it ideal for evaluating continuous numerical predictions.

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Precision

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F1 score

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Area Under the ROC Curve (AUC)

Question 11

A data scientist has a small dataset with only 200 labeled samples. They want to get a reliable estimate of model performance without using a separate validation set that would reduce the training data. Which technique should the data scientist use in Azure Machine Learning to obtain this reliable estimate?

Accepted Answer

k-fold cross-validation. B is correct because k-fold cross-validation splits the small dataset into k folds, trains the model on k-1 folds, and validates on the remaining fold, repeating this process k times. This provides a reliable performance estimate by using all 200 samples for both training and validation without requiring a separate hold-out set, which is critical for small datasets in Azure Machine Learning.

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Hold-out validation

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Data augmentation

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Principal Component Analysis (PCA)

Question 12

A data scientist is training a regression model to predict house prices in Azure Machine Learning. The model uses features like square footage, number of bedrooms, and location (zip code). The data scientist notices that the model has a very low error on the training data but a high error on the test data. Which technique should the data scientist apply during model training to reduce overfitting by penalizing large coefficients?

Accepted Answer

Use a regularization algorithm like Lasso (L1).. Lasso (L1) regularization adds a penalty equal to the absolute value of the magnitude of coefficients, which can shrink some coefficients to zero, effectively performing feature selection and reducing overfitting. This directly addresses the problem of large coefficients causing the model to fit noise in the training data, leading to high test error.

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Use a smaller test set.

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Apply feature scaling only.

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Increase the number of training epochs.

Question 13

A robotics company is training a drone to fly autonomously through an obstacle course. The drone receives positive rewards for staying on course and avoiding obstacles, and negative rewards for collisions. The system learns by trial and error to maximize its cumulative reward. Which type of machine learning is being used?

Accepted Answer

Reinforcement learning. Reinforcement learning is the correct choice because the drone learns by interacting with its environment, receiving rewards (positive for staying on course, negative for collisions), and adjusting its behavior through trial and error to maximize cumulative reward. This is the defining characteristic of reinforcement learning, where an agent learns a policy from feedback signals rather than from labeled data or hidden patterns.

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Supervised learning

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Unsupervised learning

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Semi-supervised learning

Question 14

A data scientist is building a binary classification model to predict fraudulent credit card transactions. The dataset is highly imbalanced: only 1% of transactions are fraudulent. The cost of a false negative is very high because missing a fraudulent transaction can lead to significant financial loss. Which evaluation metric should the data scientist prioritize to minimize false negatives?

Accepted Answer

Recall. Recall (also known as sensitivity or true positive rate) measures the proportion of actual positive cases (fraudulent transactions) that are correctly identified. In this highly imbalanced scenario where missing a fraud (false negative) is extremely costly, maximizing recall ensures that the model catches as many fraudulent transactions as possible, even if it means some false positives occur. This directly aligns with the goal of minimizing false negatives.

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Accuracy

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Precision

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F1 Score

Question 15

A data scientist is training a regression model to predict house prices using features like square footage, number of bedrooms, and location. After evaluating the model on a test set, the data scientist wants to select a metric that measures the average magnitude of prediction errors in the same units as the target variable (price). Which evaluation metric should the data scientist use?

Accepted Answer

Root Mean Squared Error (RMSE). Root Mean Squared Error (RMSE) is the correct metric because it measures the average magnitude of prediction errors in the same units as the target variable (price). RMSE is computed as the square root of the average squared differences between predicted and actual values, which brings the error metric back to the original unit (e.g., dollars), making it directly interpretable for regression tasks like house price prediction.

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Accuracy

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F1 Score

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Precision