A.A method that enables computers to learn from data
B.A method for manually entering data
C.A method for connecting computers to networks
D.A method for increasing computer storage
Correct Answer: A method that enables computers to learn from data
Explanation:
Machine learning enables computers to identify patterns and improve their performance using data.
Incorrect! Try again.
2What is the main purpose of training a machine learning model?
Introduction
Easy
A.To remove all data from a system
B.To replace the operating system of a computer
C.To learn patterns that support predictions
D.To increase the physical memory of a system
Correct Answer: To learn patterns that support predictions
Explanation:
Training allows a model to learn useful patterns from data and apply them to predictions.
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3In machine learning, what is a model?
Introduction
Easy
A.A learned representation used to make predictions
B.A device used to collect physical data
C.A folder used to store computer programs
D.A network used to connect databases
Correct Answer: A learned representation used to make predictions
Explanation:
A model represents patterns learned from training data and uses them to produce outputs.
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4What is training data?
Introduction
Easy
A.Data used only to install software
B.Data used only to design a website
C.Data used only to format a disk
D.Data used to teach a machine learning model
Correct Answer: Data used to teach a machine learning model
Explanation:
Training data provides the examples from which a machine learning model learns.
Incorrect! Try again.
5Which machine learning technique learns from labeled examples?
Types of machine learning techniques
Easy
A.Reinforcement learning
B.Random learning
C.Supervised learning
D.Unsupervised learning
Correct Answer: Supervised learning
Explanation:
Supervised learning uses examples that include both input data and known output labels.
Incorrect! Try again.
6Which technique is commonly used to discover groups in unlabeled data?
Types of machine learning techniques
Easy
A.Reinforcement learning
B.Supervised learning
C.Transfer learning
D.Unsupervised learning
Correct Answer: Unsupervised learning
Explanation:
Unsupervised learning identifies patterns or groups without relying on known labels.
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7Which technique learns through rewards and penalties?
Types of machine learning techniques
Easy
A.Batch learning
B.Supervised learning
C.Unsupervised learning
D.Reinforcement learning
Correct Answer: Reinforcement learning
Explanation:
Reinforcement learning trains an agent by providing rewards or penalties for its actions.
Incorrect! Try again.
8Which technique uses both labeled and unlabeled training examples?
Types of machine learning techniques
Easy
A.Reinforcement learning
B.Semi-supervised learning
C.Supervised learning
D.Unsupervised learning
Correct Answer: Semi-supervised learning
Explanation:
Semi-supervised learning combines a small amount of labeled data with unlabeled data.
Incorrect! Try again.
9Which learning problem predicts a category such as "spam" or "not spam"?
Learning problems and system
Easy
A.Dimensionality reduction
B.Classification
C.Clustering
D.Regression
Correct Answer: Classification
Explanation:
Classification assigns an input to one of several predefined categories.
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10Which learning problem predicts a continuous value such as house price?
Learning problems and system
Easy
A.Classification
B.Regression
C.Association
D.Clustering
Correct Answer: Regression
Explanation:
Regression predicts numerical values that can vary continuously, such as prices.
Incorrect! Try again.
11Which learning problem places similar data points into groups?
Learning problems and system
Easy
A.Classification
B.Ranking
C.Regression
D.Clustering
Correct Answer: Clustering
Explanation:
Clustering organizes similar data points into groups without requiring predefined labels.
Incorrect! Try again.
12What is overfitting in a machine learning system?
Learning problems and system
Easy
A.The model uses too few input features
B.The model learns training data but performs poorly on new data
C.The model stores too little training data
D.The model trains for exactly one cycle
Correct Answer: The model learns training data but performs poorly on new data
Explanation:
Overfitting occurs when a model fits the training examples too closely and does not generalize well.
Incorrect! Try again.
13What should usually be identified first when designing a learning system?
Designing a learning system
Easy
A.The file name
B.The learning task
C.The processor brand
D.The screen color
Correct Answer: The learning task
Explanation:
A learning system should begin with a clear definition of the task it must perform.
Incorrect! Try again.
14Why is relevant training data collected when designing a learning system?
Designing a learning system
Easy
A.To teach the model useful patterns
B.To change the computer operating system
C.To increase the monitor resolution
D.To rename the project files
Correct Answer: To teach the model useful patterns
Explanation:
Relevant training data helps the model learn patterns connected to the intended task.
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15What is a feature in a machine learning system?
Designing a learning system
Easy
A.A backup copy of the trained program
B.An input attribute used by the model
C.A final score assigned after testing
D.A hardware device connected to a computer
Correct Answer: An input attribute used by the model
Explanation:
A feature is a measurable input property that the model uses for learning or prediction.
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16Why is test data used after training a model?
Designing a learning system
Easy
A.To select the computer's operating system
B.To add labels to every training example
C.To evaluate performance on unseen examples
D.To increase the size of every feature
Correct Answer: To evaluate performance on unseen examples
Explanation:
Test data checks how well the trained model performs on examples it did not learn from.
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17In the definition of a learning problem, what does the task describe?
Concept of learning task
Easy
A.The job the system must perform
B.The language used for documentation
C.The number of files in a folder
D.The computer used to store data
Correct Answer: The job the system must perform
Explanation:
The task states what the learning system is expected to do, such as classify emails.
Incorrect! Try again.
18In a learning task, what does experience refer to?
Concept of learning task
Easy
A.The hardware selected for deployment
B.The color chosen for the interface
C.The folder containing the application
D.The data or interactions used for learning
Correct Answer: The data or interactions used for learning
Explanation:
Experience is the information from which the system learns, such as examples or interactions.
Incorrect! Try again.
19What is the purpose of a performance measure in a learning task?
Concept of learning task
Easy
A.To define the format of every file
B.To evaluate how well the system performs
C.To count the number of installed programs
D.To choose the physical size of a computer
Correct Answer: To evaluate how well the system performs
Explanation:
A performance measure provides a way to judge whether the system is improving at its task.
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20For an email spam detector, what is the learning task?
Concept of learning task
Easy
A.Measuring the computer's storage capacity
B.Classifying emails as spam or not spam
C.Grouping computers by processor speed
D.Predicting the length of an email address
Correct Answer: Classifying emails as spam or not spam
Explanation:
The task of a spam detector is to assign each email to the spam or not-spam category.
Incorrect! Try again.
21A model achieves 98% accuracy on its training data but only 72% accuracy on previously unseen data. Which conclusion is most appropriate?
Introduction
Medium
A.The model has eliminated prediction variance
B.The model is underfitting the training data
C.The model is overfitting the training data
D.The model requires no further evaluation
Correct Answer: The model is overfitting the training data
Explanation:
The large gap between training and unseen-data performance indicates overfitting: the model learned training-specific patterns that do not generalize well.
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22A dataset contains house area, number of rooms, neighborhood, and selling price. If the goal is to predict selling price, how should these variables be used?
Introduction
Medium
A.All variables are targets with no input features
B.House area is the target; the others are features
C.Selling price is the target; the others are features
D.Neighborhood is the target; the others are features
Correct Answer: Selling price is the target; the others are features
Explanation:
The value being predicted is the target, while the variables used to make that prediction are input features.
Incorrect! Try again.
23A bank trains a model using transactions labeled as either fraudulent or legitimate. Which learning technique is being used?
Types of machine learning techniques
Medium
A.Reinforcement learning
B.Supervised learning
C.Association learning
D.Unsupervised learning
Correct Answer: Supervised learning
Explanation:
Supervised learning uses labeled examples that provide the correct output for each training instance.
Incorrect! Try again.
24A retailer wants to divide customers into groups based on purchasing behavior, but no customer-group labels are available. Which technique is most suitable?
Types of machine learning techniques
Medium
A.Classification
B.Reinforcement learning
C.Regression
D.Clustering
Correct Answer: Clustering
Explanation:
Clustering is an unsupervised technique that discovers groups of similar observations without requiring predefined labels.
Incorrect! Try again.
25A warehouse robot improves its navigation by receiving positive rewards for fast deliveries and penalties for collisions. Which technique best describes this process?
Types of machine learning techniques
Medium
A.Linear regression
B.Unsupervised clustering
C.Reinforcement learning
D.Supervised classification
Correct Answer: Reinforcement learning
Explanation:
Reinforcement learning trains an agent through rewards and penalties produced by its interactions with an environment.
Incorrect! Try again.
26A company has 500 labeled medical images and 20,000 unlabeled images. It wants to use both sets to train a classifier. Which approach is most appropriate?
Types of machine learning techniques
Medium
A.Purely unsupervised learning
B.Semi-supervised learning
C.Reinforcement learning
D.Purely supervised learning
Correct Answer: Semi-supervised learning
Explanation:
Semi-supervised learning combines a small labeled dataset with a larger unlabeled dataset to improve predictive performance.
Incorrect! Try again.
27Which pairing correctly matches a prediction problem with its supervised learning type?
Loan default has discrete outcomes, such as default or no default, so it is a classification problem.
Incorrect! Try again.
28A polynomial model performs poorly on both its training set and test set. What is the most likely learning problem?
Learning problems and system
Medium
A.Data leakage
B.Underfitting
C.Overfitting
D.Concept drift
Correct Answer: Underfitting
Explanation:
Poor performance on both training and test data usually means the model is too simple or insufficiently trained to capture the underlying pattern.
Incorrect! Try again.
29A model trained to predict employee resignations uses a feature indicating whether an exit interview was completed. Why is this feature problematic?
Learning problems and system
Medium
A.It creates unsupervised labels
B.It causes class balancing
C.It reduces feature dimensionality
D.It introduces target leakage
Correct Answer: It introduces target leakage
Explanation:
An exit interview is generally known only after resignation, so it exposes information unavailable when a real prediction must be made.
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30An email classifier worked well when deployed, but its accuracy gradually declined as users adopted new vocabulary and writing styles. What is the most likely cause?
Learning problems and system
Medium
A.Concept drift
B.Feature scaling
C.Random initialization
D.Label encoding
Correct Answer: Concept drift
Explanation:
Concept drift occurs when the relationship between input data and expected outputs changes over time.
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31Two classes contain 9,900 legitimate transactions and 100 fraudulent transactions. Why can 99% accuracy be misleading?
Learning problems and system
Medium
A.Fraud labels become continuous values
B.Training examples lose their features
C.Accuracy always ignores correct predictions
D.A majority-only model can achieve it
Correct Answer: A majority-only model can achieve it
Explanation:
Predicting every transaction as legitimate would achieve 99% accuracy while detecting none of the fraudulent transactions.
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32A team must choose among several model configurations. Which dataset allocation best supports unbiased final evaluation?
Designing a learning system
Medium
A.Train and tune on test data, then evaluate on validation data
B.Train and evaluate on all data, then create a test set
C.Train on validation data, tune on test data, and evaluate on training data
D.Train on training data, tune on validation data, and evaluate on test data
Correct Answer: Train on training data, tune on validation data, and evaluate on test data
Explanation:
Separating training, validation, and test roles prevents the final test set from influencing model fitting or configuration choices.
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33For a disease-screening system, failing to identify an affected patient is much more costly than issuing an extra warning. Which metric should receive the greatest emphasis?
Designing a learning system
Medium
A.Compression ratio
B.Training speed
C.Specificity
D.Recall
Correct Answer: Recall
Explanation:
Recall measures the proportion of actual positive cases detected, so emphasizing it helps reduce false negatives.
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34A speech-recognition model will serve users in many regions. Which training-data strategy is most appropriate?
Designing a learning system
Medium
A.Use only studio recordings from one frequently represented region
B.Select recordings that produce the highest training accuracy
C.Sample speakers, accents, and environments expected after deployment
D.Exclude uncommon accents to make the dataset more consistent
Correct Answer: Sample speakers, accents, and environments expected after deployment
Explanation:
Training data should represent the deployment population and operating conditions so that the model can generalize to actual users.
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35A linear model cannot capture a curved relationship between two variables. Which design change most directly addresses this limitation?
Designing a learning system
Medium
A.Evaluate repeatedly on the same test observations
B.Introduce nonlinear features or use a nonlinear model
C.Increase the classification threshold above its maximum
D.Remove the target values from the training dataset
Correct Answer: Introduce nonlinear features or use a nonlinear model
Explanation:
A curved relationship requires a representation or hypothesis class capable of expressing nonlinear patterns.
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36A recommendation system is retrained every week from user clicks. Which precaution best limits a feedback loop in which already-popular items dominate future recommendations?
Designing a learning system
Medium
A.Collect some interaction data through controlled exploration
B.Remove all records involving less-popular products
C.Train only on items shown by the current model
D.Rank products solely by their historical click totals
Correct Answer: Collect some interaction data through controlled exploration
Explanation:
Controlled exploration gathers evidence about less-exposed items and reduces dependence on choices made by the existing model.
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37Using the task-experience-performance framework, a chess program learns from completed games and is evaluated by the percentage of games won. What represents the experience ?
Concept of learning task
Medium
A.The available legal moves
B.The act of playing chess
C.The percentage won
D.The completed games
Correct Answer: The completed games
Explanation:
In the framework, experience is the data or interaction history from which the system learns.
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38A spam filter learns from previously labeled emails and is judged by its score on new emails. What is the learning task ?
Concept of learning task
Medium
A.Reading the collection of previously labeled emails
B.Computing the harmonic mean of precision and recall
C.Storing incoming messages in the user's mailbox
D.Classifying new emails as spam or non-spam
Correct Answer: Classifying new emails as spam or non-spam
Explanation:
The task is the activity the system must improve at, which here is assigning spam or non-spam labels.
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39A delivery-time model predicts the number of minutes required for each order. Which formulation correctly identifies its output and task type?
Concept of learning task
Medium
A.A continuous output in a regression task
B.A discrete output in a classification task
C.A reward sequence in a reinforcement task
D.An unlabeled group in a clustering task
Correct Answer: A continuous output in a regression task
Explanation:
Delivery time is a numeric quantity that can take many values, making its prediction a regression task.
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40A model minimizes mean squared error, . Which change has the greatest direct effect on this measure?
Concept of learning task
Medium
A.A prediction with a very small error
B.A label written as an integer
C.A feature with a binary value
D.A prediction with a very large error
Correct Answer: A prediction with a very large error
Explanation:
Because each error is squared, a large prediction error contributes disproportionately more to MSE than a small error.
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41Two classifiers have identical zero-one loss on a finite training set but make different predictions on unseen inputs. Which principle best explains why empirical evidence alone cannot determine which classifier will generalize better?
Introduction
Hard
A.The central limit theorem requires normally distributed labels
B.The no-free-lunch principle requires an inductive bias
C.The law of large numbers requires zero training error
D.The representer theorem requires a linear hypothesis class
Correct Answer: The no-free-lunch principle requires an inductive bias
Explanation:
Finite observations do not uniquely determine behavior on unseen inputs. Preferring one consistent classifier over another requires assumptions—an inductive bias—about the target problem.
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42Suppose labels satisfy , where and . Under squared-error loss, what is the lowest achievable expected prediction error for any function of ?
Introduction
Hard
A., because inputs vary across examples
B., because conditional noise remains unpredictable
C., because the Bayes predictor recovers every label
D., because error is measured in label units
Correct Answer: , because conditional noise remains unpredictable
Explanation:
The Bayes predictor is . Its residual is , so its expected squared error is the irreducible variance .
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43A high-capacity model's training error decreases after more features are added, while its test error increases. Assuming the evaluation data are representative and uncontaminated, which diagnosis is most defensible?
Introduction
Hard
A.The added features increased estimation variance
B.The added features corrected label imbalance
C.The added features increased approximation bias
D.The added features eliminated irreducible noise
Correct Answer: The added features increased estimation variance
Explanation:
Additional features can enlarge the effective hypothesis space. Lower training error with worse test error is consistent with increased estimation variance and overfitting.
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44For binary classification under equal misclassification costs, a calibrated model estimates . Which prediction minimizes conditional zero-one risk at , and what is that risk?
Introduction
Hard
A.Predict ; the conditional risk is
B.Predict ; the conditional risk is
C.Predict ; the conditional risk is
D.Predict ; the conditional risk is
Correct Answer: Predict ; the conditional risk is
Explanation:
The Bayes classifier selects the more probable class. Predicting is wrong only when , which has conditional probability .
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45A model learns representations by predicting masked words in millions of unlabeled documents, then is fine-tuned using labeled sentiment examples. How should the two phases be classified?
Types of machine learning techniques
Hard
A.Unsupervised clustering followed by active learning
B.Supervised learning followed by transfer-free inference
C.Reinforcement learning followed by semi-supervised learning
D.Self-supervised learning followed by supervised learning
Correct Answer: Self-supervised learning followed by supervised learning
Explanation:
Masked-word targets are generated from the input itself, making pretraining self-supervised. Sentiment fine-tuning uses externally supplied labels and is supervised.
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46A recommendation system selects one item, observes a reward only for that item, and does not model long-term state transitions. Which learning formulation most precisely matches this setting?
Types of machine learning techniques
Hard
A.Unsupervised ranking with implicit clusters
B.Full-information online supervised learning
C.Contextual bandit learning with partial feedback
D.Model-based reinforcement learning with delayed states
Correct Answer: Contextual bandit learning with partial feedback
Explanation:
The system chooses an action from context and observes only that action's reward. With no sequential state dynamics, this is a contextual bandit rather than full reinforcement learning.
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47A semi-supervised classifier assumes that nearby points usually share labels. Adding many unlabeled examples reduces its test accuracy. Which data geometry most directly explains this failure?
Types of machine learning techniques
Hard
A.Labeled examples uniformly cover every cluster
B.Class boundaries lie only in low-density regions
C.Class boundaries pass through high-density regions
D.Unlabeled examples match labeled class proportions
Correct Answer: Class boundaries pass through high-density regions
Explanation:
Many semi-supervised methods rely on smoothness or low-density separation. If true class boundaries cross dense regions, unlabeled structure can reinforce an incorrect decision boundary.
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48In pool-based active learning, which condition most seriously undermines uncertainty sampling even when the classifier's probability estimates are calibrated on its current labeled set?
Types of machine learning techniques
Hard
A.The labeled set contains examples from every class
B.The unlabeled pool contains out-of-distribution regions
C.The labeling oracle is costly but consistently accurate
D.The hypothesis class contains several nonlinear models
Correct Answer: The unlabeled pool contains out-of-distribution regions
Explanation:
A model may be uncertain on irrelevant or out-of-distribution points. Querying those points can consume the labeling budget without improving performance on the target distribution.
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49A hospital model predicts readmission using a feature named discharge_summary_length. At deployment, predictions must be produced upon admission, before any discharge summary exists. What is the primary defect?
Learning problems and system
Hard
A.The model contains target leakage through unavailable future information
B.The model contains covariate shift through changing patient demographics
C.The model contains underfitting through insufficient feature complexity
D.The model contains class imbalance through rare readmission labels
Correct Answer: The model contains target leakage through unavailable future information
Explanation:
The feature is created after the prediction time and may encode events related to the outcome. It is unavailable in the operational system, causing temporal target leakage.
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50Training and deployment satisfy but . If support overlap holds, which correction directly targets deployment risk?
Learning problems and system
Hard
A.Weight each training loss by
B.Weight each training loss by
C.Weight each training loss by
D.Weight each training loss by
Correct Answer: Weight each training loss by
Explanation:
Under covariate shift, importance weighting by the test-to-training input density ratio converts an expectation under the training distribution into one under the deployment distribution.
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51A fraud model is retrained weekly from labels that arrive 60 days after each transaction. A sudden fraud-strategy change occurs today. Which system limitation most directly delays adaptation?
Learning problems and system
Hard
A.Batch inference prevents individual probability estimates
B.Model calibration preserves the previous decision threshold
The system cannot learn the new relationship until outcomes become available. Frequent retraining does not solve concept drift when supervisory labels arrive much later.
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52A classifier retains the same ranking of all examples after deployment, but its predicted probabilities become systematically too large. Which property can remain unchanged while calibration degrades?
Learning problems and system
Hard
A.Negative log-likelihood
B.Brier score
C.Area under the ROC curve
D.Expected calibration error
Correct Answer: Area under the ROC curve
Explanation:
ROC AUC depends only on ranking. Systematically distorted probabilities can preserve every ranking while worsening calibration error, Brier score, and log-loss.
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53A preprocessing pipeline imputes missing values and standardizes features using the complete dataset before cross-validation. Why is the resulting cross-validation estimate optimistically biased?
Designing a learning system
Hard
A.Each fold's model is optimized using a different loss function
B.Each fold's labels are stratified using the global class proportion
C.Each fold's training process uses statistics from its validation fold
D.Each fold's validation process uses fewer features than its training fold
Correct Answer: Each fold's training process uses statistics from its validation fold
Explanation:
Imputation and scaling parameters must be fitted inside each training fold. Global preprocessing leaks validation-fold information into training and biases performance estimates.
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54A team compares 100 hyperparameter configurations by five-fold cross-validation and reports the best configuration's mean score as final performance. Which procedure best reduces selection bias while still tuning the model?
Designing a learning system
Hard
A.Nested cross-validation with tuning restricted to inner folds
B.Bootstrap resampling of the original best fold predictions
C.Leave-one-out validation after selecting the highest score
D.Repeated training on all folds with the chosen parameters
Correct Answer: Nested cross-validation with tuning restricted to inner folds
Explanation:
Nested cross-validation separates model selection from performance estimation. Inner folds tune hyperparameters, while untouched outer folds estimate the selected procedure's generalization.
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55A calibrated fraud model outputs . Investigating a transaction costs , while missing fraud costs ; a successful investigation prevents the entire fraud loss. When should the system investigate?
Designing a learning system
Hard
A.Investigate when
B.Investigate when
C.Investigate when
D.Investigate when
Correct Answer: Investigate when
Explanation:
Investigation has cost , while not investigating has expected cost . Investigation is preferred exactly when .
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56Users who receive a model-triggered intervention become less likely to exhibit the measured adverse outcome. Training future models directly on these observed outcomes can create which system-level problem?
Designing a learning system
Hard
A.Random label noise independent of model predictions
B.Intervention-induced feedback that changes observed labels
C.Static class imbalance unrelated to deployed decisions
D.Pure covariate shift with unchanged conditional outcomes
Correct Answer: Intervention-induced feedback that changes observed labels
Explanation:
Model decisions alter outcomes, so observed labels no longer reveal what would have happened without intervention. This feedback can hide high-risk cases from later training data.
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57Using the formulation in which a program learns from experience with respect to tasks and performance measure , which specification is invalid because it confuses the task with the measure?
Correct Answer: : minimize RMSE; : predict prices; : sale records
Explanation:
Predicting prices is the task, whereas RMSE is a performance measure. The option reverses the roles of and .
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58In noise-free concept learning, the version space is the set of hypotheses consistent with every observed example. After adding one correctly labeled example, which statement must hold?
Concept of learning task
Hard
A.The version space must lose exactly one hypothesis
B.The version space must retain only the target concept
C.The version space can only shrink or remain unchanged
D.The version space can expand if the example is redundant
Correct Answer: The version space can only shrink or remain unchanged
Explanation:
A new consistency constraint can eliminate hypotheses but cannot make a previously inconsistent hypothesis consistent. A redundant example may leave the version space unchanged.
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59A concept learner searches only conjunctions of Boolean literals, but the true target is XOR of two attributes. With unlimited noise-free data, what prevents the learner from reaching zero population error?
Concept of learning task
Hard
A.The labels violate stationarity in the target distribution
B.The examples violate independence in the sampling process
C.The learner violates calibration under zero-one loss
D.The target violates realizability in the hypothesis class
Correct Answer: The target violates realizability in the hypothesis class
Explanation:
XOR cannot be represented by a single conjunction of literals. The learner therefore has approximation error even with unlimited clean data.
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60A finite hypothesis class is evaluated in a realizable PAC setting using a consistent learner. Which sample-size order is sufficient to achieve error at most with probability at least ?
Concept of learning task
Hard
A.
B.
C.
D.
Correct Answer:
Explanation:
For a consistent learner in the realizable finite-class case, a union bound gives sample complexity proportional to .
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