AI-900 Describe fundamental principles of machine learning on Azure Practice Test 5 — 15 Questions

Question 1

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 2

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 3

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 4

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 5

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 6

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

Question 7

A data scientist trains a machine learning model to predict house prices based on features like square footage, number of bedrooms, and location. The model achieves a very low error on the training data but performs poorly on a held-out test set. Which term best describes this situation?

Accepted Answer

Overfitting. The model performs exceptionally well on training data but poorly on test data, which is the classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training set rather than generalizing to unseen data. In Azure Machine Learning, this can be detected by monitoring the gap between training and validation metrics, and mitigated using techniques like regularization or early stopping.

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Underfitting

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High bias

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High variance

Question 8

A retail company has historical data about customers, including age, purchase history, and whether they have churned (yes/no). They want to train a model that predicts if a new customer will churn. Which type of machine learning should they use?

Accepted Answer

Supervised classification. The goal is to predict a categorical outcome (churn: yes/no) from historical labeled data. Supervised classification algorithms, such as logistic regression or decision trees, learn from input features (age, purchase history) and the target label (churn status) to assign new customers to one of the discrete classes. This directly matches the requirement for a binary classification model.

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Supervised regression

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Unsupervised clustering

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Reinforcement learning

Question 9

A medical research team trains a model to detect a rare disease from lab results. The disease occurs in only 1% of patients. The model predicts 'no disease' for every patient and achieves 99% accuracy. Which metric best reveals that the model is failing to identify actual disease cases?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positive cases correctly identified by the model. With a 99% accuracy but zero true positives (since the model always predicts 'no disease'), recall is 0%, which directly reveals the model's failure to detect any actual disease cases. In Azure Machine Learning, recall is a key metric for imbalanced classification tasks, especially when missing a positive case has severe consequences.

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Accuracy

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Precision

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F1 score

Question 10

A manufacturer trains a model to detect defective parts on an assembly line. Only 2% of parts are defective. The model predicts 'non-defective' for all parts and achieves 98% accuracy. Which metric best reveals the model's inability to identify defective parts?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual defective parts correctly identified by the model. With 98% accuracy but zero true positives (since the model labels everything as non-defective), recall is 0%, which directly exposes the model's failure to detect any defective parts despite high accuracy.

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Accuracy

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Precision

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F1 Score

Question 11

A data scientist trains a classification model on a dataset of 10,000 labeled emails to distinguish spam from non-spam. The model achieves 99% accuracy on the training data but only 70% accuracy on a held-out test set. Which term best describes this situation?

Accepted Answer

B) Overfitting. The model performs exceptionally well on training data (99% accuracy) but poorly on unseen test data (70% accuracy), which is the classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training set rather than generalizing to new data, often due to excessive model complexity or insufficient regularization.

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A) Underfitting

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C) Bias-variance tradeoff

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D) Regularization

Question 12

A real estate company has a dataset containing square footage, number of bedrooms, and location for 10,000 houses, along with their sale prices. They want to train a model that predicts the sale price of a new house based on these features. Which type of machine learning should they use?

Accepted Answer

Supervised regression. The goal is to predict a continuous numeric value (sale price) from input features (square footage, bedrooms, location). This is a classic supervised regression problem because the training data includes labeled target values (prices) and the output is a real number, not a category.

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Supervised classification

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Unsupervised clustering

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Reinforcement learning

Question 13

A data scientist is evaluating a binary classification model that predicts whether a transaction is fraudulent. The test set contains 1,000 transactions: 990 legitimate and 10 fraudulent. The model's predictions are shown in the confusion matrix below. Confusion matrix: Predicted Legitimate Predicted Fraudulent Actual Legitimate 942 48 Actual Fraudulent 2 8 Which metric should the data scientist prioritize if the business goal is to minimize the number of fraudulent transactions that are missed (false negatives)?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positives correctly identified, calculated as TP/(TP+FN). With 2 false negatives (missed fraudulent transactions), recall is 8/(8+2)=0.80. Minimizing missed fraud directly corresponds to maximizing recall, making it the correct priority for this business goal.

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Precision

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Accuracy

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Specificity

Question 14

A retail company wants to analyze customer purchase histories to identify natural groups of customers with similar buying patterns. They do not have predefined categories. Which type of machine learning should they use?

Accepted Answer

Unsupervised clustering. Unsupervised clustering is the correct approach because the company wants to discover natural groupings in customer purchase histories without predefined labels. Clustering algorithms, such as K-Means or DBSCAN, partition data into clusters based on feature similarity, enabling the identification of customer segments with similar buying patterns without any prior training on labeled examples.

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Reinforcement learning

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Supervised classification

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Supervised regression

Question 15

A real estate company trains a model to predict house prices. They evaluate it on a test set of 100 houses. The model predictions have a mean absolute error (MAE) of $5,000 and a root mean squared error (RMSE) of $20,000. What does the large difference between MAE and RMSE indicate about the model's errors?

Accepted Answer

The model has many small errors and a few large errors.. The mean absolute error (MAE) of $5,000 and root mean squared error (RMSE) of $20,000 show a large discrepancy because RMSE squares errors before averaging, which heavily penalizes large deviations. Since RMSE is four times larger than MAE, this indicates that while most predictions are close (small errors), there are a few predictions with very large errors that inflate the RMSE. This pattern is classic for a model that performs well on most houses but fails badly on a few outliers.

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The model consistently overestimates prices.

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The model has a high bias and low variance.

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The model is perfectly accurate.