AI-900 Describe fundamental principles of machine learning on Azure Practice Test 7 — 15 Questions

Question 1

A data scientist trains a regression model to predict energy consumption for a smart building. The model achieves very low error on the training data but performs significantly worse on a held-out validation set. Which technique would most directly address this problem?

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

Accepted Answer

Regularization. The model's low training error but high validation error indicates overfitting, where the model has memorized the training data rather than learning generalizable patterns. Regularization (e.g., L1 or L2) directly penalizes large coefficients, reducing model complexity and improving generalization to unseen data.

Answer

Feature engineering

Answer

Cross-validation

Answer

Hyperparameter tuning

Question 2

A data scientist trains a binary classification model to detect a rare disease. The dataset contains 99% negative cases and only 1% positive cases. The model predicts all cases as negative, achieving an accuracy of 99% on the test set. However, the business requires the model to identify as many positive cases as possible. Which metric should the data scientist examine to best reveal that the model is failing to identify any positive cases?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positive cases correctly identified by the model. With all predictions as negative, recall is 0%, directly revealing the model's failure to detect any positive cases despite the high accuracy.

Answer

Precision

Answer

F1 score

Answer

AUC-ROC

Question 3

An e-commerce company has a dataset of customer purchase histories with no predefined categories. The data analyst wants to identify natural groupings of customers based on their purchasing behavior to target marketing campaigns. Which type of machine learning should the analyst use?

Accepted Answer

Clustering. Clustering is the correct choice because it is an unsupervised learning technique used to discover inherent groupings in data without predefined labels. In this scenario, the analyst wants to identify natural customer segments based on purchase behavior, which aligns perfectly with clustering algorithms like K-Means or DBSCAN that partition data into clusters of similar patterns.

Answer

Regression

Answer

Classification

Answer

Reinforcement learning

Question 4

A data scientist trains a regression model to predict house prices. The model performs poorly on both the training data and the test data, showing high error in both sets. Which concept 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 error on both the training and test sets. In this regression scenario, the model fails to learn the relationship between features and house prices, leading to poor performance across all data splits.

Answer

Overfitting

Answer

Data leakage

Answer

Feature scaling

Question 5

A data scientist is training a binary classification model to detect rare equipment failures from sensor data. The dataset contains 99.5% normal operation readings and only 0.5% failure readings. The model currently predicts all readings as 'normal' and achieves 99.5% accuracy on the test set. The business requires the model to identify at least 80% of actual failures. Which data-level technique should the data scientist use to most directly address the class imbalance?

Accepted Answer

Oversample the minority class (failure examples). Oversampling the minority class (failure examples) directly addresses the severe class imbalance by creating synthetic copies or duplicates of the rare failure instances. This balances the training dataset, allowing the model to learn patterns associated with failures rather than always predicting the majority class. With a balanced dataset, the model can be trained to meet the business requirement of identifying at least 80% of actual failures, even though overall accuracy may decrease.

Answer

Undersample the majority class (normal examples)

Answer

Use precision as the optimization metric

Answer

Reduce the complexity of the model

Question 6

A hospital deploys a machine learning model to screen patients for a rare disease. Only 0.1% of patients actually have the disease. The model correctly identifies most positive cases but also flags many healthy patients as potentially having the disease. The hospital wants to minimize the number of healthy patients who are incorrectly told they might have the disease. Which metric should the model optimize?

Accepted Answer

Precision. Precision measures the proportion of positive identifications that are actually correct. In this scenario, the hospital wants to minimize false positives (healthy patients incorrectly told they might have the disease). Optimizing precision directly reduces false positives, which is the stated goal.

Answer

Recall

Answer

F1 score

Answer

Accuracy

Question 7

A data scientist trains a binary classification model to detect fraudulent transactions. The dataset contains only 2% fraudulent transactions. The model achieves 98% overall accuracy, but it fails to detect any fraudulent transactions, classifying all transactions as legitimate. Which metric would most clearly reveal this failure?

Accepted Answer

Recall. Recall (also known as sensitivity or true positive rate) measures the proportion of actual positive cases (fraudulent transactions) that were correctly identified by the model. In this scenario, the model classifies all transactions as legitimate, so it detects zero fraudulent transactions, yielding a recall of 0%. Despite 98% overall accuracy, the recall metric clearly exposes the model's complete failure to identify any fraud.

Answer

Precision

Answer

F1 score

Answer

Specificity

Question 8

A city's traffic department wants to predict the number of cars that will cross a particular bridge each day to plan maintenance schedules. The output of the model should be a numerical value representing the estimated traffic count. Which type of machine learning task is this?

Accepted Answer

Regression. Regression is the correct type of machine learning task because the goal is to predict a continuous numerical value—the number of cars crossing the bridge each day. Unlike classification, which predicts discrete categories, regression models output a real number, making it ideal for forecasting traffic counts.

Answer

Classification

Answer

Clustering

Answer

Reinforcement learning

Question 9

A data scientist trains a regression model to predict energy consumption for a smart building. The model achieves very low error on the training data but performs significantly worse on a held-out validation set. Which technique would most directly address this problem?

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

Accepted Answer

Overfitting. The model performs well on training data but poorly on new data from a different city, which is the classic symptom of overfitting. Overfitting occurs when a model learns noise and specific patterns in the training data that do not generalize to unseen data, especially when the new data has different characteristics (e.g., different property market dynamics). In this case, the model has memorized the training city's price patterns rather than learning generalizable relationships.

Answer

Underfitting

Answer

Data leakage

Answer

Bias-variance tradeoff

Question 10

A data scientist trains a decision tree model to predict customer churn. The model achieves 99% accuracy on the training data but only 80% on the test data. Which concept best explains this performance difference?

Accepted Answer

Overfitting. The model's high accuracy on training data (99%) but significantly lower accuracy on test data (80%) indicates that it has memorized the training data rather than learning generalizable patterns. This is the classic symptom of overfitting, where the decision tree captures noise and outliers in the training set, leading to poor performance on unseen data.

Answer

Underfitting

Answer

Bias-variance tradeoff

Answer

Cross-validation

Question 11

A data scientist trains a model to predict the exact number of cars that will cross a bridge each day for maintenance planning. The model uses historical traffic data as input. Which type of machine learning task is this?

Accepted Answer

Regression. The model predicts a continuous numerical value (the exact number of cars) based on historical traffic data. Regression is the correct machine learning task for predicting continuous numeric outcomes, such as counts, prices, or temperatures, making option B correct.

Answer

Classification

Answer

Clustering

Answer

Reinforcement learning

Question 12

A data scientist is developing a classification model to detect fraudulent transactions. The dataset is split into training and test sets. The data scientist repeatedly tunes the model's hyperparameters and evaluates performance on the test set until the test accuracy reaches 95%. However, when the model is deployed on new, unseen data, its accuracy drops to 70%. Which concept best explains this performance degradation?

Accepted Answer

Overfitting to the test set. The data scientist repeatedly tuned hyperparameters based on test set performance, effectively using the test set as part of the training process. This causes the model to become specialized to the test set's specific patterns and noise, so it fails to generalize to new, unseen data. This phenomenon is known as overfitting to the test set, where the test set no longer provides an unbiased estimate of real-world performance.

Answer

Overfitting to the training data

Answer

Data leakage from the training set to the test set

Answer

Underfitting the training data

Question 13

A data scientist trains a regression model to predict energy consumption for a smart building. The model achieves very low error on the training data but performs significantly worse on a held-out validation set. Which technique would most directly address this problem?

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

Accepted Answer

Root Mean Squared Error (RMSE). Root Mean Squared Error (RMSE) is the correct metric because it squares the residuals before averaging, which disproportionately amplifies the impact of large errors (outliers) compared to small errors. This aligns directly with the business requirement to heavily penalize large prediction errors in the regression model for daily electricity consumption.

Answer

Mean Absolute Error (MAE)

Answer

R-squared

Answer

Mean Absolute Percentage Error (MAPE)

Question 14

What is machine learning?

Accepted Answer

A subset of AI where algorithms learn from data to make predictions without explicit programming. Machine learning is a subset of artificial intelligence (AI) that enables systems to automatically learn and improve from experience without being explicitly programmed for every scenario. Instead of following static rules, ML algorithms use training data to identify patterns and make predictions or decisions. This is the core definition tested in AI-900, distinguishing ML from traditional rule-based programming.

Answer

A process of manually programming computers with rules for every possible scenario

Answer

A method of creating robots that can perform physical tasks

Answer

A type of computer network for processing large datasets

Question 15

Which type of machine learning uses labeled training data where the correct output is provided for each input?

Accepted Answer

Supervised learning. Supervised learning is the correct answer because it explicitly uses labeled training data where each input example is paired with the correct output label. The algorithm learns to map inputs to outputs by minimizing the error between its predictions and the provided labels, enabling tasks like classification and regression.

Answer

Unsupervised learning

Answer

Reinforcement learning

Answer

Transfer learning