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MCQOPTIONS
This section includes 940 Mcqs, each offering curated multiple-choice questions to sharpen your Artificial Intelligence knowledge and support exam preparation. Choose a topic below to get started.
| 501. |
This clustering algorithm initially assumes that each data instance represents a single cluster. |
| A. | agglomerative clustering |
| B. | conceptual clustering |
| C. | k-means clustering |
| D. | expectation maximization |
| Answer» D. expectation maximization | |
| 502. |
This clustering algorithm merges and splits nodes to help modify nonoptimal partitions. |
| A. | agglomerative clustering |
| B. | expectation maximization |
| C. | conceptual clustering |
| D. | k-means clustering |
| Answer» E. | |
| 503. |
This technique associates a conditional probability value with each data instance. |
| A. | linear regression |
| B. | logistic regression |
| C. | simple regression |
| D. | multiple linear regression |
| Answer» C. simple regression | |
| 504. |
This supervised learning technique can process both numeric and categorical input attributes. |
| A. | linear regression |
| B. | bayes classifier |
| C. | logistic regression |
| D. | backpropagation learning |
| Answer» B. bayes classifier | |
| 505. |
With Bayes classifier, missing data items are |
| A. | treated as equal compares. |
| B. | treated as unequal compares. |
| C. | replaced with a default value. |
| D. | ignored. |
| Answer» C. replaced with a default value. | |
| 506. |
The leaf nodes of a model tree are |
| A. | averages of numeric output attribute values. |
| B. | nonlinear regression equations. |
| C. | linear regression equations. |
| D. | sums of numeric output attribute values. |
| Answer» D. sums of numeric output attribute values. | |
| 507. |
Logistic regression is a ________ regression technique that is used to model data having a_____outcome. |
| A. | linear, numeric |
| B. | linear, binary |
| C. | nonlinear, numeric |
| D. | nonlinear, binary |
| Answer» E. | |
| 508. |
Regression trees are often used to model _______ data. |
| A. | linear |
| B. | nonlinear |
| C. | categorical |
| D. | symmetrical |
| Answer» C. categorical | |
| 509. |
Simple regression assumes a __________ relationship between the input attribute and outputattribute. |
| A. | linear |
| B. | quadratic |
| C. | reciprocal |
| D. | inverse |
| Answer» B. quadratic | |
| 510. |
The correlation coefficient for two real-valued attributes is –0.85. What does this value tell you? |
| A. | the attributes are not linearly related. |
| B. | as the value of one attribute increases the value of the second attribute also increases. |
| C. | as the value of one attribute decreases the value of the second attribute increases. |
| D. | the attributes show a curvilinear relationship. |
| Answer» D. the attributes show a curvilinear relationship. | |
| 511. |
The average squared difference between classifier predicted output and actual output. |
| A. | mean squared error |
| B. | root mean squared error |
| C. | mean absolute error |
| D. | mean relative error |
| Answer» B. root mean squared error | |
| 512. |
Bootstrapping allows us to |
| A. | choose the same training instance several times. |
| B. | choose the same test set instance several times. |
| C. | build models with alternative subsets of the training data several times. |
| D. | test a model with alternative subsets of the test data several times. |
| Answer» B. choose the same test set instance several times. | |
| 513. |
Selecting data so as to assure that each class is properly represented in both the training andtest set. |
| A. | cross validation |
| B. | stratification |
| C. | verification |
| D. | bootstrapping |
| Answer» C. verification | |
| 514. |
Data used to optimize the parameter settings of a supervised learner model. |
| A. | training |
| B. | test |
| C. | verification |
| D. | validation |
| Answer» E. | |
| 515. |
The standard error is defined as the square root of this computation. |
| A. | the sample variance divided by the total number of sample instances. |
| B. | the population variance divided by the total number of sample instances. |
| C. | the sample variance divided by the sample mean. |
| D. | the population variance divided by the sample mean. |
| Answer» B. the population variance divided by the total number of sample instances. | |
| 516. |
The average positive difference between computed and desired outcome values. |
| A. | root mean squared error |
| B. | mean squared error |
| C. | mean absolute error |
| D. | mean positive error |
| Answer» E. | |
| 517. |
Which of the following is a common use of unsupervised clustering? |
| A. | detect outliers |
| B. | determine a best set of input attributes for supervised learning |
| C. | evaluate the likely performance of a supervised learner model |
| D. | determine if meaningful relationships can be found in a dataset |
| Answer» B. determine a best set of input attributes for supervised learning | |
| 518. |
Which statement is true about prediction problems? |
| A. | the output attribute must be categorical. |
| B. | the output attribute must be numeric. |
| C. | the resultant model is designed to determine future outcomes. |
| D. | the resultant model is designed to classify current behavior. |
| Answer» E. | |
| 519. |
Classification problems are distinguished from estimation problems in that |
| A. | classification problems require the output attribute to be numeric. |
| B. | classification problems require the output attribute to be categorical. |
| C. | classification problems do not allow an output attribute. |
| D. | classification problems are designed to predict future outcome. |
| Answer» D. classification problems are designed to predict future outcome. | |
| 520. |
Imagine a Newly-Born starts to learn walking. It will try to find a suitable policy to learn walking after repeated falling and getting up.specify what type of machine learning is best suited? |
| A. | classification |
| B. | regression |
| C. | kmeans algorithm |
| D. | reinforcement learning |
| Answer» E. | |
| 521. |
Another name for an output attribute. |
| A. | predictive variable |
| B. | independent variable |
| C. | estimated variable |
| D. | dependent variable |
| Answer» C. estimated variable | |
| 522. |
A nearest neighbor approach is best used |
| A. | with large-sized datasets. |
| B. | when irrelevant attributes have been removed from the data. |
| C. | when a generalized model of the data is desirable. |
| D. | when an explanation of what has been found is of primary importance. |
| Answer» C. when a generalized model of the data is desirable. | |
| 523. |
For a multiple regression model, SST = 200 and SSE = 50. The multiple coefficient ofdetermination is |
| A. | 0.25 |
| B. | 4.00 |
| C. | 0.75 |
| D. | none of the above |
| Answer» C. 0.75 | |
| 524. |
The adjusted multiple coefficient of determination accounts for |
| A. | the number of dependent variables in the model |
| B. | the number of independent variables in the model |
| C. | unusually large predictors |
| D. | none of the above |
| Answer» E. | |
| 525. |
The multiple coefficient of determination is computed by |
| A. | dividing ssr by sst |
| B. | dividing sst by ssr |
| C. | dividing sst by sse |
| D. | none of the above |
| Answer» D. none of the above | |
| 526. |
A measure of goodness of fit for the estimated regression equation is the |
| A. | multiple coefficient of determination |
| B. | mean square due to error |
| C. | mean square due to regression |
| D. | none of the above |
| Answer» D. none of the above | |
| 527. |
A multiple regression model has |
| A. | only one independent variable |
| B. | more than one dependent variable |
| C. | more than one independent variable |
| D. | none of the above |
| Answer» C. more than one independent variable | |
| 528. |
A multiple regression model has the form: y = 2 + 3x1 + 4x2. As x1 increases by 1 unit (holding x2constant), y will |
| A. | increase by 3 units |
| B. | decrease by 3 units |
| C. | increase by 4 units |
| D. | decrease by 4 units |
| Answer» D. decrease by 4 units | |
| 529. |
A term used to describe the case when the independent variables in a multiple regression modelare correlated is |
| A. | regression |
| B. | correlation |
| C. | multicollinearity |
| D. | none of the above |
| Answer» D. none of the above | |
| 530. |
A regression model in which more than one independent variable is used to predict thedependent variable is called |
| A. | a simple linear regression model |
| B. | a multiple regression models |
| C. | an independent model |
| D. | none of the above |
| Answer» D. none of the above | |
| 531. |
Supervised learning differs from unsupervised clustering in that supervised learning requires |
| A. | at least one input attribute. |
| B. | input attributes to be categorical. |
| C. | at least one output attribute. |
| D. | output attributes to be categorical. |
| Answer» C. at least one output attribute. | |
| 532. |
Supervised learning and unsupervised clustering both require at least one |
| A. | hidden attribute. |
| B. | output attribute. |
| C. | input attribute. |
| D. | categorical attribute. |
| Answer» B. output attribute. | |
| 533. |
Computers are best at learning |
| A. | facts. |
| B. | concepts. |
| C. | procedures. |
| D. | principles. |
| Answer» B. concepts. | |
| 534. |
The process of forming general concept definitions from examples of concepts to belearned. |
| A. | deduction |
| B. | abduction |
| C. | induction |
| D. | conjunction |
| Answer» D. conjunction | |
| 535. |
The effectiveness of an SVM depends upon: |
| A. | selection of kernel |
| B. | kernel parameters |
| C. | soft margin parameter c |
| D. | all of the above |
| Answer» E. | |
| 536. |
What can be major issue in Leave-One-Out-Cross-Validation(LOOCV)? |
| A. | low variance |
| B. | high variance |
| C. | faster runtime compared to k-fold cross validation |
| D. | slower runtime compared to normal validation |
| Answer» C. faster runtime compared to k-fold cross validation | |
| 537. |
A student Grade is a variable F1 which takes a value from A,B,C and D. Which of the following is True in the following case? |
| A. | variable f1 is an example of nominal variable |
| B. | variable f1 is an example of ordinal variable |
| C. | it doesn\t belong to any of the mentioned categories |
| D. | it belongs to both ordinal and nominal category |
| Answer» C. it doesn\t belong to any of the mentioned categories | |
| 538. |
PCA works better if there is1. A linear structure in the data2. If the data lies on a curved surface and not on a flat surface3. If variables are scaled in the same unit |
| A. | 1 and 2 |
| B. | 2 and 3 |
| C. | 1 and 3 |
| D. | 1,2 and 3 |
| Answer» D. 1,2 and 3 | |
| 539. |
You are given sesimic data and you want to predict next earthquake , this is an example of |
| A. | supervised learning |
| B. | reinforcement learning |
| C. | unsupervised learning |
| D. | dimensionality reduction |
| Answer» B. reinforcement learning | |
| 540. |
Which of the folllowing is an example of feature extraction? |
| A. | construction bag of words from an email |
| B. | applying pca to project high dimensional data |
| C. | removing stop words |
| D. | forward selection |
| Answer» C. removing stop words | |
| 541. |
The most popularly used dimensionality reduction algorithm is Principal Component Analysis (PCA). Which of the following is/are true about PCA? 1. PCA is an unsupervised method2. It searches for the directions that data have the largest variance3. Maximum number of principal components |
| A. | 1 & 2 |
| B. | 2 & 3 |
| C. | 3 & 4 |
| D. | all of the above |
| Answer» E. | |
| 542. |
It is not necessary to have a target variable for applying dimensionality reduction algorithms |
| A. | true |
| B. | false |
| Answer» B. false | |
| 543. |
Feature can be used as a |
| A. | binary split |
| B. | predictor |
| C. | both a and b?? |
| D. | none of the above |
| Answer» D. none of the above | |
| 544. |
A measurable property or parameter of the data-set is |
| A. | training data |
| B. | feature |
| C. | test data |
| D. | validation data |
| Answer» C. test data | |
| 545. |
Following are the descriptive models |
| A. | clustering |
| B. | classification |
| C. | association rule |
| D. | both a and c |
| Answer» E. | |
| 546. |
If machine learning model output doesnot involves target variable then that model is called as |
| A. | descriptive model |
| B. | predictive model |
| C. | reinforcement learning |
| D. | all of the above |
| Answer» B. predictive model | |
| 547. |
In simple term, machine learning is |
| A. | training based on historical data |
| B. | prediction to answer a query |
| C. | both a and b?? |
| D. | automization of complex tasks |
| Answer» D. automization of complex tasks | |
| 548. |
The "curse of dimensionality" referes |
| A. | all the problems that arise when working with data in the higher dimensions, that did not exist in the lower dimensions. |
| B. | all the problems that arise when working with data in the lower dimensions, that did not exist in the higher dimensions. |
| C. | all the problems that arise when working with data in the lower dimensions, that did not exist in the lower dimensions. |
| D. | all the problems that arise when working with data in the higher dimensions, that did not exist in the higher dimensions. |
| Answer» B. all the problems that arise when working with data in the lower dimensions, that did not exist in the higher dimensions. | |
| 549. |
Select the correct answers for following statements.1. Filter methods are much faster compared to wrapper methods.2. Wrapper methods use statistical methods for evaluation of a subset of features while Filter methods use cross validation. |
| A. | both are true |
| B. | 1 is true and 2 is false |
| C. | both are false |
| D. | 1 is false and 2 is true |
| Answer» C. both are false | |
| 550. |
Which learning Requires Self Assessment to identify patterns within data? |
| A. | unsupervised learning |
| B. | supervised learning |
| C. | semisupervised learning |
| D. | reinforced learning |
| Answer» B. supervised learning | |