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

Question 1

A data scientist trains a regression model to predict the selling price of houses. After evaluating on a test set, the data scientist wants a metric that measures the average absolute error between predicted and actual prices, expressed in the same units (dollars) as the target variable. Which evaluation metric should the data scientist use?

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

Accepted Answer

Mean Absolute Error (MAE). Mean Absolute Error (MAE) is the correct metric because it directly measures the average absolute difference between predicted and actual house prices, and its result is expressed in the same unit (dollars) as the target variable. This makes it intuitive for stakeholders to understand the typical prediction error in monetary terms.

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R-squared (R²)

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Root Mean Squared Error (RMSE)

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Mean Squared Error (MSE)

Question 2

A data scientist is training a model to classify customer reviews as positive, negative, or neutral. The dataset contains 10,000 reviews, but only 500 of them are negative. The data scientist wants to ensure the model performs well on the minority class (negative reviews). Which technique should the data scientist consider to address the class imbalance?

Accepted Answer

Use a resampling technique like SMOTE or random oversampling of the minority class. Resampling techniques like SMOTE (Synthetic Minority Oversampling Technique) or random oversampling directly address class imbalance by generating synthetic samples or duplicating existing samples from the minority class (negative reviews). This balances the training dataset, preventing the model from being biased toward the majority class (positive/neutral reviews) and improving recall for the minority class.

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Increase the learning rate

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Add more features to the model

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Use L1 regularization (Lasso)

Question 3

A data scientist wants to group customers into segments based on purchasing behavior without using any labeled examples. Which type of machine learning is this?

Accepted Answer

Unsupervised learning. Unsupervised learning is the correct choice because the data scientist has no labeled examples and wants to discover hidden patterns or groupings in the data. Clustering algorithms, such as K-Means or DBSCAN, are used to segment customers based solely on their purchasing behavior features, without any predefined categories.

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Supervised learning

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Reinforcement learning

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Semi-supervised learning

Question 4

A data scientist is using Azure Automated Machine Learning to build a binary classification model for a highly imbalanced dataset (95% negative, 5% positive). The data scientist wants AutoML to select the best model based on a metric that is robust to class imbalance. Which primary metric should the data scientist configure in the AutoML settings?

Accepted Answer

AUC_weighted. AUC_weighted is the correct primary metric for imbalanced binary classification because it computes the area under the ROC curve for each class and averages them with weight proportional to the class support. This weighting ensures that the metric reflects performance on both the majority (95% negative) and minority (5% positive) classes, making it robust to severe imbalance. Azure Automated Machine Learning uses AUC_weighted as a recommended metric when the dataset is skewed, as it penalizes models that ignore the minority class.

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Accuracy

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F1_score

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Log_loss

Question 5

A data scientist is preparing a dataset to train a model that predicts customer churn. The dataset includes a column 'CustomerID' which is a unique identifier for each customer. Should the data scientist include the 'CustomerID' column as a feature in the training data?

Accepted Answer

No, because the CustomerID is a random unique identifier with no predictive power for churn.. CustomerID is a unique identifier that does not contain any meaningful pattern or relationship with the target variable (churn). Including such a column would introduce noise and risk overfitting, as the model could memorize each ID rather than learning generalizable patterns. In Azure Machine Learning, features should be predictive attributes, not arbitrary labels.

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Yes, because it uniquely identifies each customer and helps the model differentiate them.

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Yes, because the model can learn patterns from the numeric values.

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No, because the CustomerID column contains too many missing values.

Question 6

A retail company wants to automatically group customers into segments based on their purchasing history, age, and location without using any predefined labels. The goal is to identify distinct customer profiles for targeted marketing campaigns. Which type of machine learning approach should they use?

Accepted Answer

Unsupervised learning. Unsupervised learning is the correct approach because the company wants to group customers into segments without predefined labels. The algorithm will discover natural patterns and clusters in the data (purchasing history, age, location) on its own, which is the core characteristic of unsupervised learning.

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Supervised learning

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Reinforcement learning

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Regression

Question 7

A data scientist is training a model to predict whether a customer will purchase a product (Yes/No). The dataset contains 90% 'No' and 10% 'Yes'. After training, the model achieves 90% accuracy. Which evaluation metric would be more informative to assess the model's performance on the minority class?

Accepted Answer

F1 score. In this imbalanced dataset (90% 'No', 10% 'Yes'), a model that always predicts 'No' would achieve 90% accuracy, making accuracy a misleading metric. The F1 score is the harmonic mean of precision and recall, specifically designed to evaluate a model's performance on the minority class by balancing false positives and false negatives. It is the most informative metric here because it directly measures how well the model identifies the rare 'Yes' purchases without being inflated by the majority class.

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Mean Absolute Error (MAE)

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Area Under the Curve (AUC)

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R-squared

Question 8

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 9

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 10

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 11

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 12

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 13

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 14

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 15

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