Application of Machine Learning Multiple Choice Questions


Hello friends in this article we will discuss about Application of machine learning Multiple Choice Questions and answers. In various entrance exams and College exams these questions are asked so you can refer these questions for your Study purpose. 

Application of Machine Learning Multiple Choice Questions

1) ………. is movment of goods

A. Logistics 

B. Transportation

C. Distribution 

D. None of these 

Ans. B 


2) Which of the following are public services?

A. Record Keeping 

B. Policy Making

C. Citizen Services 

D. All of the above 

Ans : D 


3) ……… technique is used to access large amount of data

A. Outbreak Prediction 

B. Record Keeping

C. Data Crowdsourcing 

D. Trade Settlement

Ans : C 


4) Which of the following are applications of ML in healthcare?

A. Accelerating medical research 

B. Smart health records

C. Research and clinical trials 

D. All of the above 

Ans : D 


5) …………. is the measurement,collection,analysis and reporting of data about learners and their context for the purpose of understanding as well as optimizing learning

A. Predictive Analytics 

B. Learning Analytics

C. Adaptive Learning 

D. Personalized Learning 

Ans : B 


6) AODE stands for ……….

A. Application Of Dependence Estimator

B. Averaged One-Dependence Energy

C. Averaged One-Deployment Energy

D. Averaged One-Dependence Estimator 

Ans : D

7) RBF stands for ………..

A. Radial Basis Function 

B. Road Basic Food

C. Radial Black Fund 

D. Red Blood Flower 

Ans : A 


8) Semi-supervised means ………..

A. Category of the machine learning approaches and create to control of labelled or

unlabelled data for instructions

B. It is defined by its use of labelled datasets to train algorithms that to classify data or predict

outcomes accurately

C. Unsupervised learning(UL) is a type of algorithm that learns patterns from untagged data

D. Both A and C 

Ans : A 


9) Which are the types of reinforcement learning?

A. Negative RL 

B. Posstive RL

C. Semi Negative RL 

D. Both A and B 

Ans : D 


10) Which of the following is true about reinforcement learning?

A. The agent gets reward or penalty according to the action

B. It’s an online learning

C. The target of an agent is to maximize the rewards

D. All of the above 

Ans : D 


11) What is unsupervised learning?

A. All data is unlabelled and the algorithms learn to inherent structure from the input data

B. All data is labelled and the algorithms learn to predict the output from the input data

C. It is a framework for learning where an agent interacts with an environment and receives ar e ward for each interaction

D. Some data is labelled but most of it is unlabelled and a mixture of supervised and unsupervised techniques can be used 

Ans : A 


12) Features of AODE

A. It supports incremental learning

B. AODE has computational complexity at training time

C. This makes it infeasible for application to high dimensional data

D. All of the above 

Ans : D 


13) In what type of learning labeled training data is required

A. Unsupervised Learning 

B. Supervised Learning

C. Reinforcement Learning 

D. Semi-supervised Learning 

Ans : B 



14) KNN is the which type of ML algorithm?

A. Supervised Machine Learning 


B. Unsupervised Machine Learning

C. Reinforcement Machine Learning 

D. None of the above

Ans : B 



15) What is supervised learning?

A. All data is unlabelled and the algorithms learn to inherent structure from the input data

B. All data is labelled and the algorithms learn to predict the output from the input data

C. It is a framework for learning where an agent interacts with an environment and receives a reward for each interaction

D. Some data is labelled but most of it is unlabelled and a mixture of supervised and unsupervised techniques can be used 

Ans : B 


16) What is semi-supervised learning?

A. All data is unlabelled and the algorithms learn to inherent structure from the input data

B. All data is labelled and the algorithms learn to predict the output from the input data

C. It is a framework for learning where an agent interacts with an environment and receives a reward for each interaction

D. Some data is labelled but most of it is unlabelled and a mixture of supervised and unsupervised techniques can be used 

Ans : D 


17) What is reinforcement learning?

A. All data is unlabelled and the algorithms learn to inherent structure from the input data

B. All data is labelled and the algorithms learn to predict the output from the input data

C. It is a framework for learning where an agent interacts with an environment and receives a reward for each interaction

D. Some data is labelled but most of it is unlabelled and a mixture of supervised and unsupervised techniques can be used 

Ans : C

18) A regression model in which more than one independent variable is used to predict the dependent variable is called …..

A. A simple linear regression model 

B. A multiple regression models

C. An independent model 

D. None of the above 

Ans : C 



19) There are 2 types of supervised machine learning are ………. and ……….

A. Clustering, Association 

B. Regression, Association

C. Clustering, Classification 

D. Classification, Regression 

Ans : D 



20) There are 2 types of unsupervised machine learning are ………. and ……….

A. Clustering, Association

B. Regression, Association

C. Clustering, Classification 

D. Classification, Regression 

Ans : A 



21) CART stands for ……….

A. Clustering And Regression Tree

B. Classification And Regression Tree

C. Canonical And Regression Tree

D. None of the above 

Ans : B 



22) Real-time decisions, Game AI, Learning tasks, Skill acquisition and Robot navigation are applications of which of the following

A. Unsupervised Learning 

B. Supervised Learning

C. Semi-supervised Learning 

D. Reinforcement Learning 

Ans : D 



23) Extension of Naive Bayes is called ……….

A. Gaussian Naive Bayes 

B. Bayes Classifier 

C. Random Forest 

D. Decision Tree 

24) Who is the father of Machine Learning?

A. Geoffrey Hill 

B. Geoffrey Chaucer

C. Geoffrey Everest Hinton 

D. None of the above 

Ans : C 




25) Which of the following is true about Naive Bayes?

A. Assumes that all the features in a dataset are equally important

B. Assumes that all the features in a dataset are independent

C. Both A and B

D. None of the above 

Ans : C





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