AI-900 Describe fundamental principles of machine learning on Azure Practice Test 2 — 15 Questions

Question 1

A data scientist is building a model to predict the exact temperature in degrees Celsius based on humidity and atmospheric pressure. The model will output a single numeric value for each input. Which type of machine learning task is this?

Accepted Answer

Regression. This is a regression task because the goal is to predict a continuous numeric value (temperature in degrees Celsius) from input features (humidity and atmospheric pressure). Regression models output a real number, unlike classification which predicts discrete categories. In Azure Machine Learning, regression algorithms like Linear Regression or Decision Forest Regression are used for such tasks.

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Classification

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Clustering

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Object detection

Question 2

A media company wants to automatically organize a large collection of news articles into several topic-based categories (e.g., politics, sports, technology) without using any predefined labels. They plan to use Azure Machine Learning. Which type of machine learning task should they use?

Accepted Answer

Clustering. Clustering is the correct choice because the media company wants to group unlabeled news articles into topic-based categories based on inherent similarities in the data, without using predefined labels. Azure Machine Learning provides clustering algorithms like K-Means that automatically partition the dataset into distinct clusters, making it ideal for unsupervised learning tasks where the goal is to discover natural groupings.

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Regression

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Classification

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Anomaly detection

Question 3

A retail company wants to segment its customers into different groups based on purchasing behavior, without using predefined categories. Which type of machine learning task should they use?

Accepted Answer

Clustering. Clustering is the correct choice because it is an unsupervised learning technique that groups data points based on inherent similarities without requiring predefined labels. In this scenario, the retail company wants to discover natural segments in customer purchasing behavior, such as high-frequency buyers or discount seekers, without providing any existing categories. Azure Machine Learning offers clustering algorithms like K-Means, which iteratively assigns customers to clusters by minimizing within-cluster variance based on features like purchase frequency and average order value.

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Classification

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Regression

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Reinforcement learning

Question 4

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?

Accepted Answer

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.

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Increase the number of training epochs

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Add more hidden layers

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Increase the learning rate

Question 5

An online retailer wants to build a recommendation system that learns from user interactions. The system suggests a product, and if the user clicks it, it receives a positive reward; if ignored, a negative reward. Over time, the system learns to make better suggestions. Which type of machine learning best describes this approach?

Accepted Answer

Reinforcement learning. Reinforcement learning is correct because the system learns by interacting with its environment (user clicks) and receiving rewards (positive for clicks, negative for ignores) to maximize cumulative reward over time. This trial-and-error feedback loop, without explicit labeled data, is the hallmark of reinforcement learning.

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Supervised learning

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Unsupervised learning

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Semi-supervised learning

Question 6

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?

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 7

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 8

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 9

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 10

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. 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.

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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 11

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 12

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 13

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 14

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 15

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. The correct answer is A, 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