MCQs

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Overall MCQs for Reference

Which technique is primarily used in predictive modeling to discover patterns in large datasets?
a. Data mining
b. Data compression
c. Data recovery
d. Data labeling
Answer: a. Data miningscribd+1
Predictive Analytics aims to:
a. Only guess current values
b. Retrieve and use data to predict future results
c. Ignore historical data
d. Forecast only qualitative outcomes
Answer: b. Retrieve and use data to predict future resultslearn.microsoft+1
Which method involves associating patterns in historical data with future outcomes?
a. Descriptive analytics
b. Diagnostic analytics
c. Predictive modeling
d. Prescriptive modeling
Answer: c. Predictive modelingtestbook+1
What is usually the first step in building a predictive model?
a. Model validation
b. Data collection and preprocessing
c. Model deployment
d. Data publishing
Answer: b. Data collection and preprocessinggeeksforgeeks
Which algorithm is commonly used for classification in predictive analytics?
a. Decision Tree
b. K-Means
c. Apriori
d. PCA
Answer: a. Decision Treeinterviewkickstart+1
In predictive modeling, splitting the dataset is crucial for:
a. Data encryption
b. Parallel processing
c. Model evaluation
d. Data entry
Answer: c. Model evaluationgeeksforgeeks
What is the main difference between supervised and unsupervised learning?
a. Supervised needs labeled data
b. Unsupervised gives numeric outputs
c. Unsupervised uses graphics
d. Supervised never predicts
Answer: a. Supervised needs labeled datapreplaced
In predictive analytics, what does overfitting indicate?
a. Model is too simplistic
b. Model captures all patterns and noise
c. Model never learns
d. Model lacks data
Answer: b. Model captures all patterns and noiseinsightsoftware+1
Which predictive model technique is resistant to overfitting and handles complex datasets?
a. Logistic regression
b. Decision tree
c. Random Forest
d. Linear regression
Answer: c. Random Forestinsightsoftware
What type of output is generated by binary prediction models?
a. Only categorical with more than two classes
b. Numeric range
c. Yes or No outcome
d. Descriptive text
Answer: c. Yes or No outcomelearn.microsoft
Which two errors should be balanced in predictive modeling?
a. Type I and Type II
b. Bias and variance
c. Syntax and runtime
d. Model and data
Answer: b. Bias and varianceinterviewkickstart+1
The main advantage of ensemble methods like Random Forest is:
a. Always faster
b. Lower data requirements
c. Reducing variance and improving accuracy
d. Eliminating the need for training
Answer: c. Reducing variance and improving accuracyinsightsoftware
Which is not a typical application of predictive analytics?
a. Fraud detection
b. Weather forecasting
c. Sentiment analysis
d. Image compression
Answer: d. Image compressionqlik+1
Which approach generates multiple subsets with possible repetition for robust predictions?
a. Boosting
b. Bagging
c. Clustering
d. Normalization
Answer: b. Bagginginsightsoftware
In predictive modeling, the model trained only on historical data is called:
a. Descriptive model
b. Predictive model
c. Vision model
d. Simulation model
Answer: b. Predictive modeltestbook+1
A model that identifies the probability of a specific event is what type?
a. Diagnostic
b. Predictive
c. Descriptive
d. Prescriptive
Answer: b. Predictiveqlik+1
What is cross-validation used for in predictive modeling?
a. Data cleaning
b. Model assessment
c. Data storage
d. Visualization
Answer: b. Model assessmentgeeksforgeeks
Linear regression is mainly used for which type of prediction?
a. Binary
b. Multi-class
c. Numerical
d. Textual
Answer: c. Numericalqlik+1
The “target variable” in predictive modeling refers to:
a. Input features
b. Output to be predicted
c. Data format
d. The largest dataset
Answer: b. Output to be predictedtestbook+1
In predictive analytics, the subset used to adjust model parameters is:
a. Training set
b. Test set
c. Validation set
d. Backup set
Answer: c. Validation setgeeksforgeeks
Which is a disadvantage of overfitting in predictive models?
a. Inability to generalize
b. Lacks accuracy with training
c. Fast performance
d. Simple structure
Answer: a. Inability to generalizetestbook+1
Which metric is commonly used for classification performance?
a. Mean squared error
b. F1 score
c. Entropy
d. Variance
Answer: b. F1 scoretestbook+1
Model interpretability helps with:
a. Increasing computation
b. Reducing results
c. Understanding learned relationships
d. Slowing predictions
Answer: c. Understanding learned relationshipsqlik
Which algorithm can perform both regression and classification?
a. Random Forest
b. K-Means
c. PCA
d. Apriori
Answer: a. Random Forestinsightsoftware
Which of the following is NOT a step in predictive modeling?
a. Feature engineering
b. Model validation
c. Data encryption
d. Data preprocessing
Answer: c. Data encryptiongeeksforgeeks
A confusion matrix is used to measure the performance of:
a. Regression models
b. Clustering models
c. Classification models
d. Time series models
Answer: c. Classification modelsgeeksforgeeks
Which method is best for predicting groups with unknown classes?
a. Unsupervised learning
b. Supervised learning
c. Reinforcement learning
d. Random sampling
Answer: a. Unsupervised learningpreplaced
The act of reducing input features while retaining model performance is called:
a. Feature engineering
b. Dimensionality reduction
c. Ensemble learning
d. Sampling
Answer: b. Dimensionality reductioninsightsoftware+1
Which approach dynamically adjusts data weight based on classification error?
a. Bagging
b. Boosting
c. Clustering
d. Normalization
Answer: b. Boostinginsightsoftware
Which of the following can cause underfitting in a predictive model?
a. Model with excessive complexity
b. Too much training
c. Model too simple for data
d. Complete data
Answer: c. Model too simple for datainterviewkickstart+1
The accuracy of a predictive model is improved by:
a. Reducing validation
b. Ignoring outliers
c. Hyperparameter tuning
d. Random guessing
Answer: c. Hyperparameter tuninggeeksforgeeks
Ensemble learning combines:
a. Only one model
b. Multiple models for better accuracy
c. Raw data only
d. Manual interpretation
Answer: b. Multiple models for better accuracyinsightsoftware
Predictive models are deployed in which phase?
a. Data preprocessing
b. Model selection
c. Production
d. Validation
Answer: c. Productiontestbook
Time-series forecasting is a kind of:
a. Prescriptive analytics
b. Predictive analytics
c. Diagnostic analytics
d. Descriptive analytics
Answer: b. Predictive analyticsqlik+1
Regularization in predictive modeling is used for:
a. Overfitting prevention
b. Random sampling
c. Data cleaning
d. Feature scaling
Answer: a. Overfitting preventioninterviewkickstart
Which of these is a common loss function for regression models?
a. Hinge loss
b. Cross-entropy
c. Mean squared error
d. F1 score
Answer: c. Mean squared errorgeeksforgeeks
Predictive analytics can be used in which field?
a. Retail
b. Finance
c. Healthcare
d. All of the above
Answer: d. All of the abovelearn.microsoft+1
Which of the following is an unsupervised algorithm?
a. Linear regression
b. K-Means
c. Logistic regression
d. Decision tree
Answer: b. K-Meanspreplaced+1
Which model type uses “hidden states” for time-dependent predictions?
a. Bayesian network
b. Markov chain
c. Hidden Markov Model
d. Neural network
Answer: c. Hidden Markov Modelsanfoundry
Data leakage in predictive modeling means:
a. Information from validation/test leaks into training
b. Data loss during transfer
c. Backup failure
d. Unlabeled data
Answer: a. Information from validation/test leaks into traininggeeksforgeeks
Which algorithm is best for predicting whether an email is spam or not?
a. Clustering
b. Classification
c. Regression
d. Reinforcement
Answer: b. Classificationlearn.microsoft
ROC curve in predictive modeling is used to:
a. Measure recall
b. Evaluate classifier performance
c. Reduce dimensionality
d. Encrypt data
Answer: b. Evaluate classifier performancegeeksforgeeks
The problem of imbalanced data affects:
a. Regression accuracy
b. Ensemble size
c. Classification performance
d. Feature count
Answer: c. Classification performancegeeksforgeeks
In predictive modeling, what is a “feature”?
a. Output variable
b. Input variable
c. Data sample
d. Loss function
Answer: b. Input variablegeeksforgeeks
Which model type best predicts continuous values?
a. Classification
b. Regression
c. Clustering
d. Association
Answer: b. Regressiongeeksforgeeks
Predictive models are validated using:
a. Training set only
b. Test set and validation set
c. Only unlabeled data
d. Data mining
Answer: b. Test set and validation setgeeksforgeeks
Early stopping is a technique in training models to:
a. Avoid overfitting
b. Increase bias
c. Reduce input size
d. Remove labels
Answer: a. Avoid overfittinggeeksforgeeks
Which is not a predictive model evaluation method?
a. Confusion matrix
b. ROC curve
c. Clustering coefficient
d. Precision-recall
Answer: c. Clustering coefficientgeeksforgeeks
The “learning rate” in optimization helps with:
a. Model size
b. Convergence speed
c. Feature selection
d. Data normalization
Answer: b. Convergence speedgeeksforgeeks
Which principle is crucial for reliable predictive model performance?
a. Training and validation separation
b. Mixing all data
c. Ignoring outputs
d. Manual analysis only
Answer: a. Training and validation separationgeeksforgeeks

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