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 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.

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Overfitting

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Data leakage

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Feature scaling

Question 2

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.

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Undersample the majority class (normal examples)

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Use precision as the optimization metric

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Reduce the complexity of the model

Question 3

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.

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Recall

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F1 score

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Accuracy

Question 4

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.

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Precision

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F1 score

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Specificity

Question 5

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.

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Classification

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Clustering

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Reinforcement learning

Question 6

A data scientist trains a regression model to predict house prices using features like bedrooms, square footage, and location. The model achieves a low error on the training data but performs significantly worse when used to predict prices in a new city with different property characteristics. Which concept best explains this poor performance?

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.

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Underfitting

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Data leakage

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Bias-variance tradeoff

Question 7

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.

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Underfitting

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Bias-variance tradeoff

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Cross-validation

Question 8

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.

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Classification

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Clustering

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Reinforcement learning

Question 9

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. Option C is correct because 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.

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Overfitting to the training data

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Data leakage from the training set to the test set

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Underfitting the training data

Question 10

A data scientist trains a regression model to predict daily electricity consumption (in kWh) for a commercial building. The business team needs a metric that heavily penalizes large prediction errors (outliers) more than small errors. Which metric should the data scientist report to best meet this requirement?

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.

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Mean Absolute Error (MAE)

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R-squared

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Mean Absolute Percentage Error (MAPE)

Question 11

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.

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

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A method of creating robots that can perform physical tasks

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A type of computer network for processing large datasets

Question 12

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.

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Unsupervised learning

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Reinforcement learning

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Transfer learning

Question 13

What is a feature in the context of machine learning?

Accepted Answer

An individual measurable property used as input to a machine learning model. In machine learning, a feature is an individual measurable property or characteristic of the data that is used as input to a model. Features are the variables that the model learns from to make predictions or classifications. This is a fundamental concept in ML, as the quality and relevance of features directly impact model performance.

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The output or prediction made by a machine learning model

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A type of neural network layer

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A software capability in Azure Machine Learning

Question 14

What is overfitting in machine learning?

Accepted Answer

When a model performs well on training data but poorly on new, unseen data. Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, resulting in high accuracy on training data but poor generalization to new, unseen data. This is a fundamental concept in ML because the goal is to create models that perform well on real-world data, not just the data they were trained on. In Azure Machine Learning, techniques like regularization, cross-validation, and early stopping are used to detect and mitigate overfitting.

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When a model is trained with too little data

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When a model takes too long to train

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When a model performs poorly on both training and test data

Question 15

What is a training dataset in machine learning?

Accepted Answer

The labeled data used to teach a machine learning model. Option B is correct because a training dataset is the labeled data used to teach a machine learning model by allowing it to learn patterns and relationships between features and labels. In Azure Machine Learning, this dataset is fed into an algorithm during the training step, where the model adjusts its internal parameters (e.g., weights in a neural network) to minimize prediction error. Without labeled training data, supervised learning models cannot learn the mapping from inputs to outputs.

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A dataset used to evaluate a trained model's performance on unseen data

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Data that has been cleaned and normalized for analysis

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Real-world data used after model deployment