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Artificial Intelligence MCQ (Multiple Choice Questions) - SchoolingAxis

Artificial Intelligence MCQ (Multiple Choice Questions) - SchoolingAxis

Artificial Intelligence MCQ (Multiple Choice Questions)

 Que- Which of the following is not an application of learning? 

a. Data mining 

b. WWW 

c. Speech recognition 

d. None of the mentioned  


Ans- None of the mentioned   


Que- Which of the following is the component of learning system? 

a. Goal 

b. Model 

c. Learning rules 

d. All of the mentioned  


Ans- All of the mentioned   


Que- Following is also called as exploratory learning: 

a. Supervised learning 

b. Active learning 

c. Unsupervised learning 

d. Reinforcement learning  


Ans- Unsupervised learning  


Que- Which is not a desirable property of a logical rule-based system? 

a. Locality 

b. Attachment 

c. Detachment 

d. Truth-Functionality  


Ans- Attachment  


Que- How is Fuzzy Logic different from conventional control methods? 

a. IF and THEN Approach 

b. FOR Approach 

c. WHILE Approach 

d. DO Approach  


Ans- IF and THEN Approach  


Que- In an Unsupervised learning 

a. Specific output values are given 

b. Specific output values are not given 

c. No specific Inputs are given 

d. Both inputs and outputs are given  


Ans- Specific output values are not given  


Que- Inductive learning involves finding a 

a. Consistent Hypothesis 

b. Inconsistent Hypothesis 

c. Regular Hypothesis 

d. Irregular Hypothesis  


Ans- Consistent Hypothesis  


Que- Computational learning theory analyzes the sample complexity and computational complexity of 

a. Unsupervised Learning 

b. Inductive learning 

c. Forced based learning 

d. Weak learning  


Ans- Inductive learning  


Que- If a hypothesis says it should be positive, but in fact, it is negative, we call it 

a. A consistent hypothesis 

b. A false negative hypothesis 

c. A false positive hypothesis 

d. A specialized hypothesis  


Ans- A false positive hypothesis  


Que- Neural Networks are complex ______________with many parameters. 

a. Linear Functions 

b. Nonlinear Functions 

c. Discrete Functions 

d. Exponential Functions  


Ans- Nonlinear Functions  


Que- A perceptron is a ______________ 

a. Feed-forward neural network 

b. Backpropagation algorithm 

c. Backtracking algorithm 

d. Feed Forward-backward algorithm  


Ans- Feed-forward neural network  


Que- Which of the following statement is true? 

a. Not all formal languages are context-free 

b. All formal languages are Context free 

c. All formal languages are like natural language 

d. Natural languages are context-oriented free  


Ans- Not all formal languages are context-free  


Que- Which of the following statement is not true? 

a. The union and concatenation of two context-free languages is context-free 

b. The reverse of a context-free language is context-free, but the complement need not be 

c. Every regular language is context-free because it can be described by a regular grammar 

d. The intersection two context-free languages is context-free  


Ans- The intersection two context-free languages is context-free   


Que- A 3-input neuron is trained to output a zero when the input is 110 and a one when the input is 111. After generalization, the output will be zero when and only when the input is: 

a. 000 or 110 or 011 or 101 

b. 010 or 100 or 110 or 101 

c. 000 or 010 or 110 or 100 

d. 100 or 111 or 101 or 001 


Ans- 000 or 010 or 110 or 100  


Que- A perceptron is: 

a. a single layer feed-forward neural network with pre-processing 

b. an auto-associative neural network 

c. a double layer auto-associative neural network 

d. a neural network that contains feedback  


Ans- a single layer feed-forward neural network with pre-processing  


Que- An auto-associative network is: 

a. a neural network that contains no loops 

b. a neural network that contains feedback 

c. a neural network that has only one loop 

d. a single layer feed-forward neural network with pre-processing  


Ans- a neural network that contains feedback  


Que- Which of the following is true?  (i) On average, neural networks have higher computational rates than conventional computers.  (ii) Neural networks learn by example.  (iii) Neural networks mimic the way the human brain works. 

a. All of the mentioned are true 

b. (ii) and (iii) are true 

c. (i), (ii) and (iii) are true 

d. None of the mentioned  


Ans- All of the mentioned are true  


Que- Which of the following is true for neural networks?  (i) The training time depends on the size of the network.  (ii) Neural networks can be simulated on a conventional computer.  (iii) Artificial neurons are identical in operation to biological ones. 

a. All of the mentioned 

b. (ii) is true 

c. (i) and (ii) are true 

d. None of the mentioned  


Ans- (i) and (ii) are true  


Que- What are the advantages of neural networks over conventional computers?  (i) They have the ability to learn by example  (ii) They are more fault tolerant  (iii)They are more suited for real time operation due to their high 'computational' rates 

a. (i) and (ii) are true 

b. (i) and (iii) are true 

c. Only (i) 

d. All of the mentioned  


Ans- All of the mentioned   


Que- Which of the following is true? Single layer associative neural networks do not have the ability to:  (i) perform pattern recognition  (ii) find the parity of a picture  (iii) determine whether two or more shapes in a picture are connected or not 

a. (ii) and (iii) are true 

b. (ii) is true 

c. All of the mentioned 

d. None of the mentioned  


Ans- (ii) and (iii) are true  


Que- Which is true for neural networks? 

a. It has set of nodes and connections 

b. Each node computes it's weighted input 

c. Node could be in excited state or non-excited state 

d. All of the mentioned  


Ans- All of the mentioned   


Que- Neuro software is: 

a. A software used to analyze neurons 

b. It is powerful and easy neural network 

c. Designed to aid experts in real world 

d. It is software used by Neurosurgeon  


Ans- It is powerful and easy neural network  


Que- Why is the XOR problem exceptionally interesting to neural network researchers? 

a. Because it can be expressed in a way that allows you to use a neural network 

b. Because it is complex binary operation that cannot be solved using neural networks 

c. Because it can be solved by a single layer perceptron 

d. Because it  is the simplest linearly inseparable problem that exists.  


Ans- Because it  is the simplest linearly inseparable problem that exists.   


Que- What is back propagation? 

a. It is another name given to the curvy function in the perceptron 

b. It is the transmission of error back through the network to adjust the inputs 

c. It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn 

d. None of the mentioned  


Ans- It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn  


Que- Why are linearly separable problems of interest of neural network researchers? 

a. Because they are the only class of problem that network can solve successfully 

b. Because they are the only class of problem that Perceptron can solve successfully 

c. Because they are the only mathematical functions that are continue 

d. Because they are the only mathematical functions you can draw  


Ans- Because they are the only class of problem that Perceptron can solve successfully  


Que- Which of the following is not the promise of artificial neural network? 

a. It can explain result 

b. It can survive the failure of some nodes 

c. It has inherent parallelism 

d. It can handle noise  


Ans- It can explain result  


Que- Neural Networks are complex ______________ with many parameters. 

a. Linear Functions 

b. Nonlinear Functions 

c. Discrete Functions 

d. Exponential Functions  


Ans- Linear Functions  


Que- A perceptron adds up all the weighted inputs it receives, and if it exceeds a certain value, it outputs a 1, otherwise it just outputs a 0. 

a. TRUE 

b. FALSE 

c. Sometimes - it can also output intermediate values as well 

d. Can't say  


Ans- TRUE  


Que- The name for the function in question 16 is 

a. Step function 

b. Heaviside function 

c. Logistic function 

d. Perceptron function  


Ans- Heaviside function  


Que- Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results 

a. True - this works always, and these multiple perceptrons learn to classify even complex problems 

b. False - perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do 

c. True - perceptrons can do this but are unable to learn to do it - they have to be explicitly hand-coded 

d. False - just having a single perceptron is enough  


Ans- True - perceptrons can do this but are unable to learn to do it - they have to be explicitly hand-coded  


Que- The network that involves backward links from output to the input and hidden layers is called as ____ 

a. Self organizing maps 

b. Perceptrons 

c. Recurrent neural network 

d. Multi layered perceptron  


Ans- Recurrent neural network  


Que- Which of the following is an application of NN (Neural Network)? 

a. Sales forecasting 

b. Data validation 

c. Risk management 

d. All of the mentioned  


Ans- All of the mentioned   


Que- A _________ is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. 

a. Decision tree 

b. Graphs 

c. Trees 

d. Neural Networks  


Ans- Decision tree  


Que- Decision Tree is a display of an algorithm. 

a. TRUE 

b. False  

c. Nothing can be said 

d. None of the mentioned 


Ans- TRUE  


Que- Decision Tree is 

a. Flow-Chart 

b. Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label 

c. Flow-Chart & Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label 

d. None of the mentioned  


Ans- Flow-Chart & Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label  


Que- Decision Trees can be used for Classification Tasks. 

a. TRUE 

b. False  

c. Nothing can be said 

d. None of the mentioned 


Ans- TRUE  


Que- Choose from the following that are Decision Tree nodes 

a. Decision Nodes 

b. End Nodes 

c. Chance Nodes 

d. All of the mentioned  


Ans- All of the mentioned   


Que- Decision Nodes are represented by ____________ 

a. Disks 

b. Squares 

c. Circles 

d. Triangles  


Ans- Squares  


Que- Chance Nodes are represented by, 

a. Disks 

b. Squares 

c. Circles 

d. Triangles  


Ans- Circles  


Que- End Nodes are represented by __________ 

a. Disks 

b. Squares 

c. Circles 

d. Triangles  


Ans- Triangles   


Que- Following are the advantage/s of Decision Trees. Choose that apply. 

a. Possible Scenarios can be added 

b. Use a white box model, If given result is provided by a model 

c. Worst, best and expected values can be determined for different scenarios 

d. All of the mentioned  


Ans- All of the mentioned   


Que- Which combines inductive methods with the power of first-order representations? 

a. Inductive programming 

b. Logic programming 

c. Inductive logic programming 

d. Lisp programming   


Ans- Inductive logic programming  


Que- How many reasons are available for the popularity of ILP? 

a. 1 

b. 2 

c. 3 

d. 4 


Ans- 3  


Que- Which cannot be represented by a set of attributes? 

a. Program 

b. Three-dimensional configuration of a protein molecule 

c. Agents 

d. None of the mentioned   


Ans- Three-dimensional configuration of a protein molecule  


Que- Which is an appropriate language for describing the relationships? 

a. First-order logic 

b. Propositional logic 

c. ILP 

d. None of the mentioned   


Ans- First-order logic  


Que- Which produces hypotheses that are easy to read for humans? 

a. ILP 

b. Artificial intelligence 

c. Propositional logic 

d. First-order logic   


Ans- ILP  


Que- What need to be satisfied in inductive logic programming? 

a. Constraint 

b. Entailment constraint 

c. Both Constraint & Entailment constraint 

d. None of the mentioned   


Ans- Entailment constraint  


Que- How many literals are available in top-down inductive learning methods? 

a. 1 

b. 2 

c. 3 

d. 4 


Ans- 3  


Que- Which inverts a complete resolution strategy? 

a. Inverse resolution 

b. Resolution 

c. Trilogy 

d. None of the mentioned   


Ans- Inverse resolution  


Que- Which method can't be used for expressing relational knowledge? 

a. Literal system 

b. Variable-based system 

c. Attribute-based system 

d. None of the mentioned   


Ans- Attribute-based system  


Que- Which approach is used for refining a very general rule through ILP? 

a. Top-down approach 

b. Bottom-up approach 

c. Both Top-down & Bottom-up approach 

d. None of the mentioned   


Ans- Top-down approach