AI-900 Describe fundamental principles of machine learning on Azure Practice Test 4 — 15 Questions

Question 1

A data scientist is training a regression model to predict house prices. The data scientist wants to evaluate the model using a metric that penalizes large prediction errors significantly more than small errors. Which evaluation metric should the data scientist choose?

Accepted Answer

Root Mean Squared Error (RMSE). Root Mean Squared Error (RMSE) is the correct choice because it squares the residuals before averaging, which heavily penalizes large prediction errors (outliers) more than small errors. This aligns with the requirement to penalize large errors significantly more than small ones, as the squaring operation amplifies the impact of larger deviations.

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Mean Absolute Error (MAE)

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R-squared (R²)

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Mean Absolute Percentage Error (MAPE)

Question 2

A data scientist has a dataset containing images of handwritten digits (0-9) where each image is labeled with the correct digit. The goal is to train a model that can predict the digit from a new image. Which type of machine learning approach should be used?

Accepted Answer

Classification. This is a supervised learning problem where the model must predict a discrete class label (digit 0-9) from input images. Classification algorithms, such as logistic regression or neural networks, are designed to map inputs to categorical outputs, making B the correct choice.

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Regression

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Clustering

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Reinforcement learning

Question 3

A data scientist has a dataset containing information about houses: size (sq ft), number of bedrooms, location, and the actual sale price. The goal is to train a model that predicts the price of a new house based on these features. Which type of machine learning task is this?

Accepted Answer

B) Regression. This is a regression task because the goal is to predict a continuous numeric value (the sale price) based on input features. Regression models learn the relationship between independent variables (size, bedrooms, location) and a dependent variable (price) to output a real number. In Azure Machine Learning, regression algorithms like Linear Regression, Decision Forest Regression, or Neural Network Regression would be appropriate for this scenario.

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A) Classification

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C) Clustering

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D) Reinforcement Learning

Question 4

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?

Accepted Answer

Overfitting. 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.

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Underfitting

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High bias

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High variance

Question 5

A data scientist trains a classification model to predict whether an email is spam or not. The model achieves 98% accuracy on the test set, but upon inspection, it classifies all emails as 'not spam' because the dataset has 95% non-spam emails. What is the most likely issue?

Accepted Answer

Data imbalance. The model achieves 98% accuracy by simply predicting all emails as 'not spam', which reflects the 95% majority class in the dataset. This is a classic symptom of class imbalance, where the model learns to exploit the skewed distribution rather than learning meaningful patterns to distinguish spam from non-spam. In Azure Machine Learning, techniques like SMOTE or stratified sampling are used to mitigate this issue.

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Overfitting

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Underfitting

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Feature scaling error

Question 6

A data science team trains several machine learning models for a regression task. They observe that Model A has low training error and low test error. Model B has low training error but high test error. Model C has high training error and high test error. Which model would most likely benefit from an ensemble technique that averages the predictions of multiple models?

Accepted Answer

Model B (low training error, high test error). Model B exhibits low training error but high test error, which is a classic sign of overfitting. Ensemble techniques like averaging predictions from multiple models reduce variance and improve generalization, making them most beneficial for overfit models. In Azure Machine Learning, you can use an ensemble pipeline or AutoML's VotingEnsemble to combine diverse models and lower test error.

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Model A (low training error, low test error)

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Model C (high training error, high test error)

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None of the models would benefit from an ensemble technique

Question 7

A data scientist has a dataset containing customer transaction records with features such as age, income, and purchase history, but no labels. The goal is to identify natural groupings of customers for a targeted marketing campaign. Which type of machine learning should be used?

Accepted Answer

Clustering. Clustering is the correct choice because the dataset has no labels, and the goal is to discover natural groupings of customers based on feature similarity. Unsupervised learning algorithms like K-Means or DBSCAN partition data into clusters where intra-cluster similarity is high and inter-cluster similarity is low, enabling targeted marketing without pre-existing categories.

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Classification

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Regression

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Reinforcement learning

Question 8

A data scientist is building a machine learning model to predict the number of daily bike rentals in a city based on weather data and day of the week. The target variable is a continuous integer. Which type of machine learning task is this?

Accepted Answer

Regression. The target variable is the number of daily bike rentals, which is a continuous integer (count). Predicting a continuous numeric value is a regression task. In Azure Machine Learning, regression algorithms such as Linear Regression, Decision Forest Regression, or Poisson Regression are used for this type of problem.

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Classification

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Clustering

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Anomaly Detection

Question 9

A data scientist trains a linear regression model to predict house prices. The model's training error is very high, and its test error is nearly as high. Which term best describes this situation?

Accepted Answer

Underfitting. Underfitting occurs when a model is too simple to capture the underlying patterns in the data, resulting in high training error and similarly high test error. In this linear regression scenario, the model fails to learn the relationship between features and house prices, leading to poor performance on both training and test sets.

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Overfitting

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High bias

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High variance

Question 10

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?

Accepted Answer

Apply regularization to the model. 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.

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Reduce the amount of training data

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Remove some features from the dataset

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Increase the number of layers in the neural network

Question 11

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

Cross-validation. Cross-validation (Option B) is the most effective technique to improve generalization because it evaluates the model on multiple subsets of the training data, reducing overfitting to a specific segment. By partitioning the data into folds and training/validating iteratively, cross-validation ensures the model learns patterns that are consistent across different data distributions, not just the historical segment. This directly addresses the problem of poor performance on new customer segments by providing a more robust estimate of model performance on unseen data.

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Feature engineering

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Increasing model complexity

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Using a larger learning rate

Question 12

A robotics team is training a robot to navigate a maze. The robot receives a positive reward (+10) when it reaches the exit and a negative reward (-1) every time it bumps into a wall. The robot learns to maximize its cumulative reward over multiple trials. Which type of machine learning is being used?

Accepted Answer

Reinforcement learning. The robot learns by interacting with its environment, receiving rewards (positive for reaching the exit, negative for bumping into walls), and adjusting its behavior to maximize cumulative reward over time. This trial-and-error learning process, where an agent learns a policy through feedback from its actions, is the defining characteristic of reinforcement learning.

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Supervised learning

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Unsupervised learning

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Semi-supervised learning

Question 13

A data scientist wants to train a model that predicts whether a customer will respond to a marketing offer (yes or no). The dataset includes features such as age, income, past purchase history, and the labeled outcome (responded or not responded) for previous customers. Which type of machine learning is this?

Accepted Answer

Supervised learning. This is supervised learning because the dataset includes labeled outcomes (responded or not responded) for previous customers, which the model uses to learn a mapping from input features (age, income, past purchase history) to the correct output. The goal is to predict a categorical label (yes/no), making it a classification task within supervised learning.

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Unsupervised learning

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Reinforcement learning

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Semi-supervised learning

Question 14

A data scientist trains a binary classification model to detect fraudulent transactions. The dataset contains only 1% fraudulent cases. The model predicts 'not fraudulent' for all transactions and achieves 99% accuracy. Which metric would best reveal the model's poor performance on fraud detection?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positive cases (fraudulent transactions) correctly identified by the model. With 1% fraud, a model that predicts 'not fraudulent' for all transactions will have a recall of 0% because it fails to catch any true positives, despite 99% accuracy. This makes recall the best metric to reveal the model's inability to detect fraud.

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Precision

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F1 score

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Accuracy

Question 15

A data scientist is training a model to predict whether a patient has a rare disease (1% prevalence). The model predicts 'no disease' for all patients and achieves 99% accuracy, but fails to identify any actual cases. Which metric would best reveal this failure?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positive cases correctly identified. With 1% disease prevalence and a model that predicts 'no disease' for all patients, recall is 0% because zero true positives are found. Accuracy (99%) is misleading here because the model fails to detect any rare disease cases, and recall directly exposes this failure.

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Precision

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F1 score

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Mean absolute error