MCQs

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Overall MCQs for Reference

1. Which of the following best defines parametrization in machine learning?
   a. Setting all feature values to zero
   b. Defining necessary parameters of a model
   c. Randomly choosing weights
   d. Ignoring biases
   Answer: b. Defining necessary parameters of a modelmylearningsinaiml.wordpress​
2. What role do parameters play in model training?
   a. Store output labels
   b. Serve as training data
   c. Set rules for prediction
   d. Remove loss function
   Answer: c. Set rules for predictionfunctionize​
3. In machine learning, what is typically optimized during training?
   a. Loss function
   b. Parameters
   c. Activation function
   d. Input data
   Answer: b. Parametersmylearningsinaiml.wordpress​
4. Which term is used for parameters that specify the structure of the learning model?
   a. Hyperparameters
   b. Feature values
   c. Model weights
   d. Data samples
   Answer: a. Hyperparametersdatacamp​
5. Which components are directly tuned during gradient descent?
   a. Data samples
   b. Output classes
   c. Weights and biases
   d. Activation values
   Answer: c. Weights and biasesmylearningsinaiml.wordpress​
6. What does the loss function in a machine learning model quantify?
   a. Amount of input data
   b. Model structure
   c. Performance of predictions
   d. Training speed
   Answer: c. Performance of predictionsmylearningsinaiml.wordpress​
7. In logistic regression, parameters are often referred to as:
   a. Bias
   b. Coefficients
   c. Factors
   d. Indices
   Answer: b. Coefficientssanfoundry​
8. Which parameter estimation method seeks values that maximize the likelihood function?
   a. Principal Component Analysis
   b. Maximum Likelihood Estimation
   c. Min-Max Scaling
   d. Label Encoding
   Answer: b. Maximum Likelihood Estimationpmc.ncbi.nlm.nih​
9. Parametric models are usually:
   a. Free from assumptions
   b. Data-driven
   c. Based on fixed mathematical forms
   d. Unsupervised
   Answer: c. Based on fixed mathematical formsgeeksforgeeks​
10. Which technique is commonly used for parameter optimization in machine learning?
    a. Grid Search
    b. K-means clustering
    c. Feature extraction
    d. Random Forest
    Answer: a. Grid Searchdatacamp​
11. What does regularization control in parametrization?
    a. Number of output classes
    b. Overfitting by penalizing parameter size
    c. Activation function
    d. Dataset size
    Answer: b. Overfitting by penalizing parameter sizeaimcqs​
12. In deep learning, what are the typical parameters learned?
    a. Kernel size
    b. Weights
    c. Batch size
    d. Epoch duration
    Answer: b. Weightsmylearningsinaiml.wordpress​
13. Which parameter type is set before training begins?
    a. Batch size
    b. Weights
    c. Bias
    d. Feature values
    Answer: a. Batch sizedatacamp​
14. Non-parametric models differ from parametric models because they:
    a. Assume fixed parameter count
    b. Make fewer assumptions about data form
    c. Are always supervised
    d. Require less data
    Answer: b. Make fewer assumptions about data formgeeksforgeeks​
15. What does ‘parameter tuning’ aim to achieve?
    a. Reduce model structure
    b. Optimize predictive performance
    c. Lower the number of features
    d. Increase randomness
    Answer: b. Optimize predictive performancescm​
16. Which of the following is a common hyperparameter in neural networks?
    a. Learning rate
    b. Weight coefficients
    c. Output labels
    d. Loss values
    Answer: a. Learning ratedatacamp​
17. Parameters in a model can be optimized using:
    a. Random search
    b. Maximum pooling
    c. Linear filtering
    d. Cross-validation
    Answer: a. Random searchdatacamp​
18. What does the bias parameter in a neural network influence?
    a. Input normalization
    b. Activation threshold
    c. Feature scaling
    d. Epoch duration
    Answer: b. Activation thresholdmylearningsinaiml.wordpress​
19. When is regularization in parametrization especially important?
    a. With small datasets
    b. During data augmentation
    c. To prevent overfitting
    d. When using decision trees
    Answer: c. To prevent overfittingaimcqs​
20. Which parameter alters the steepness of the activation function in logistic regression?
    a. Learning rate
    b. Weight coefficient
    c. Regularization value
    d. Bias
    Answer: b. Weight coefficientsanfoundry​
21. Which method performs parameter estimation via sampling from a defined distribution?
    a. Grid Search
    b. Monte Carlo method
    c. Linear regression
    d. Decision tree
    Answer: b. Monte Carlo methodpmc.ncbi.nlm.nih​
22. Which term refers to variables the model itself learns from data?
    a. Parameters
    b. Evaluation metrics
    c. Hyperparameters
    d. Classes
    Answer: a. Parameterstowardsdatascience​
23. Which is not a typical parameter in K-Nearest Neighbors?
    a. Number of neighbors
    b. Distance metric
    c. Bias
    d. Weight function
    Answer: c. Biasscribd​
24. Which parametrization technique uses an objective function to guide learning?
    a. Unsupervised clustering
    b. Optimization
    c. Feature selection
    d. Filtering
    Answer: b. Optimizationmylearningsinaiml.wordpress​
25. Regularization methods like L1 and L2 affect parameters by:
    a. Setting them negative
    b. Penalizing their magnitude
    c. Increasing data size
    d. Removing output labels
    Answer: b. Penalizing their magnitudeexploredatabase​
26. Parameters in models like ridge regression are specifically:
    a. Non-negative
    b. Regularized
    c. Randomly initialized
    d. Always zero
    Answer: b. Regularizedexploredatabase​
27. Which process is used to update parameters using calculated gradients?
    a. Feature extraction
    b. Gradient descent
    c. Ensemble learning
    d. Principal component analysis
    Answer: b. Gradient descentmylearningsinaiml.wordpress​
28. Which parameter type controls the complexity of a tree-based model?
    a. Number of leaves
    b. Output nodes
    c. Bias
    d. Activation function
    Answer: a. Number of leavesaimcqs​
29. Which parametrization approach seeks minimum loss over training data?
    a. Decision boundary search
    b. Loss minimization
    c. Feature scaling
    d. Output threshold
    Answer: b. Loss minimizationmylearningsinaiml.wordpress​
30. In parameter estimation, what does ‘overfitting’ indicate?
    a. Low bias, high variance
    b. High bias, low variance
    c. Both low bias and variance
    d. None
    Answer: a. Low bias, high variancegeeksforgeeks​
31. What happens to less important parameters in lasso regression as the tuning parameter increases?
    a. They increase
    b. Go close to zero
    c. Stay unchanged
    d. Become negative
    Answer: b. Go close to zeroexploredatabase​
32. Which of the following is NOT a parameter used in linear regression?
    a. Slope
    b. Intercept
    c. Bias
    d. Kernel size
    Answer: d. Kernel sizesanfoundry​
33. The optimization algorithm typically modifies which two elements in a neural classifier?
    a. Loss and input
    b. Parameters and dataset
    c. Weight matrix and bias vector
    d. Output classes
    Answer: c. Weight matrix and bias vectormylearningsinaiml.wordpress​
34. In parametric models, the number of parameters is:
    a. Fixed
    b. Depends on data size
    c. Varies after every epoch
    d. Zero
    Answer: a. Fixedgeeksforgeeks​
35. Which parameter estimation technique maximizes probability of observed data?
    a. Maximum Likelihood Estimation
    b. Batch Gradient Descent
    c. Min-Max Scaling
    d. Label Smoothing
    Answer: a. Maximum Likelihood Estimationpmc.ncbi.nlm.nih​
36. Parameters directly change which aspect of model prediction?
    a. Activation function
    b. Data splitting
    c. Prediction accuracy
    d. Output labels
    Answer: c. Prediction accuracyfunctionize​
37. Which parameter influences the convergence speed in model training?
    a. Learning rate
    b. Loss function
    c. Weight initialization
    d. Batch normalizer
    Answer: a. Learning ratedatacamp​
38. Which method adjusts model parameters based on error minimization?
    a. Feature engineering
    b. Optimization
    c. Data augmentation
    d. Batch normalization
    Answer: b. Optimizationmylearningsinaiml.wordpress​
39. A parametric approach is preferred when:
    a. No assumptions can be made
    b. Data is limited
    c. Data is abundant
    d. Unsupervised tasks
    Answer: b. Data is limitedgeeksforgeeks​
40. Which function evaluates parameter effectiveness after each epoch?
    a. Activation function
    b. Loss function
    c. Output threshold
    d. Split criterion
    Answer: b. Loss functionmylearningsinaiml.wordpress​
41. Parameterized learning relies on adjusting which matrix for a classifier?
    a. Input data
    b. Weight matrix
    c. Feature matrix
    d. Label matrix
    Answer: b. Weight matrixmylearningsinaiml.wordpress​
42. What does ‘parameter space’ refer to in model tuning?
    a. Number of classes
    b. Possible values parameters can take
    c. Model accuracy range
    d. Output range
    Answer: b. Possible values parameters can taketowardsdatascience​
43. Hyperparameters differ from parameters in that they are:
    a. Learned during training
    b. Tuned before training
    c. Labeled by output
    d. Regularized
    Answer: b. Tuned before trainingdatacamp​
44. In support vector machines, which parameter acts as a regularization control?
    a. C parameter
    b. Epochs
    c. Learning rate
    d. Weight decay
    Answer: a. C parameteraimcqs​
45. The number of clusters in K-Means is an example of:
    a. Model parameter
    b. Activation value
    c. Hyperparameter
    d. Bias
    Answer: c. Hyperparameteraimcqs​
46. Which parameter estimation technique uses prior knowledge and updates after seeing data?
    a. Bayesian estimation
    b. Maximum likelihood
    c. Linear regression
    d. Grid search
    Answer: a. Bayesian estimationpmc.ncbi.nlm.nih​
47. What is the outcome of improper parameter estimation?
    a. High accuracy
    b. Poor generalization
    c. Improved regularization
    d. Randomized output
    Answer: b. Poor generalizationfunctionize​
48. In parameterized models, the mapping from features to outputs is defined by:
    a. Number of epochs
    b. Parameter values
    c. Output accuracy
    d. Loss function
    Answer: b. Parameter valuesmylearningsinaiml.wordpress​
49. A key benefit of parametric models is that they:
    a. Always outperform non-parametric
    b. Simplify computation
    c. Require more data
    d. Avoid regularization
    Answer: b. Simplify computationgeeksforgeeks​
50. Parameters are updated during training to:
    a. Decrease loss
    b. Increase variance
    c. Lower number of features
    d. Ignore output labels
    Answer: a. Decrease lossmylearningsinaiml.wordpress​

1. https://mylearningsinaiml.wordpress.com/deep-learning/dl4cv/parameterized-learning/
2. https://www.functionize.com/blog/understanding-tokens-and-parameters-in-model-training
3. https://www.datacamp.com/tutorial/parameter-optimization-machine-learning-models
4. https://www.sanfoundry.com/1000-machine-learning-questions-answers/
5. https://pmc.ncbi.nlm.nih.gov/articles/PMC10041091/
6. https://www.geeksforgeeks.org/machine-learning/parametric-vs-non-parametric-models-in-machine-learning/
7. https://aimcqs.com/machine-learning
8. https://www.scm.com/doc/params/examples/intro_to_params.html
9. https://towardsdatascience.com/parameters-and-hyperparameters-aa609601a9ac/
10. https://www.scribd.com/document/513570416/ACFrOgCJFUigNteKuu50LJxpBGcbPjZqsROcKBB5PyM5KE-Q-oAs-YZJ3IADaoXffYVg95KJOlVhH3Y7i3EhDpSdahDSH67nvSoQ0jRcRPfnxznydI9KqW1NG5BlF4MYakTohHZdry6b99-9xv5H
11. https://www.exploredatabase.com/2021/06/machine-learning-multiple-choice-quiz-questions-set-27.html
12. https://www.geeksforgeeks.org/quizzes/machine-learning-quiz-questions-and-answers/
13. https://www.gyansetu.in/blog/machine-learning-mcq-interview-preparation/
14. https://www.igmguru.com/blog/machine-learning-mcqs
15. https://towardsdatascience.com/essential-parameter-estimation-techniques-in-machine-learning-and-signal-processing-d671c6607aa0/
16. https://askfilo.com/user-question-answers-smart-solutions/ai-and-machine-learning-multiple-choice-questions-1-which-3337303933363438
17. https://www.ibm.com/think/topics/model-parameters
18. https://www.machinelearningmastery.com/parametric-and-nonparametric-machine-learning-algorithms/