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AI Notes Artificial intelligence multiple-choice questions with answers A+
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AI Notes Artificial intelligence multiple-choice questionswithanswers A+
1. 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
View Answer
Answer: a
Explanation: Refer the definition of Decision tree
2. Decision Tree is a display of an algorithm.
a) True
b) False
View Answer
Answer: a
Explanation: None.
3. What is Decision Tree?
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
View Answer
Answer: c
Explanation: Refer the definition of Decision tree.
4. Decision Trees can be used for Classification Tasks.
a) True
b) False
View Answer
, Answer: a
Explanation: None.
5. Choose from the following that are Decision Tree nodes?
a) Decision Nodes
b) End Nodes
c) Chance Nodes
d) All of the mentioned
View Answer
Answer: d
Explanation: None.
6. Decision Nodes are represented by
a) Disks
b) Squares
c) Circles
d) Triangles
View Answer
Answer: b
Explanation: None.
7. Chance Nodes are represented by
a) Disks
b) Squares
c) Circles
d) Triangles
View Answer
Answer: c
Explanation: None
8. End Nodes are represented by
a) Disks
b) Squares
c) Circles
,d) Triangles
View Answer
Answer: d
Explanation: None.
9. Which of the following are the advantage/s of Decision Trees?
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
View Answer
Answer: d
Explanation: None.
1. 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.
Answer: d
Explanation: None.
2. 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
View Answer
Answer: c
Explanation: Back propagation is the transmission of error back through the network to allow weights to be
adjusted so that the network can learn.
3. 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
View Answer
, Answer: b
Explanation: Linearly separable problems of interest of neural network researchers because they are the only
class of problem that Perceptron can solve successfully.
4. 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
View Answer
Answer: a
Explanation: The artificial Neural Network (ANN) cannot explain result.
5. Neural Networks are complex with many parameters.
a) Linear Functions
b) Nonlinear Functions
c) Discrete Functions
d) Exponential Functions
View Answer
Answer: a
Explanation: Neural networks are complex linear functions with many parameters.
6. 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
View Answer
Answer: a
Explanation: Yes the perceptron works like that.
7. What is the name of the function in the following statement “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) Step function
b) Heaviside function
c) Logistic function
d) Perceptron function
View Answer
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