AI-Learning (MCQ)

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Learning – 1

1. What will take place as the agent observes its interactions with the world?
a) Learning
b) Hearing
c) Perceiving
d) Speech
View Answer
Answer: a
Explanation: Learning will take place as the agent observes its interactions with the world and itsown decision making process.

2. Which modifies the performance element so that it makes better decision?
a) Performance element
b) Changing element
c) Learning element
d) None of the mentioned
View Answer
Answer: c
Explanation: A learning element modifies the performance element so that it can make better decision.

3. How many things are concerned in the design of a learning element?
a) 1
b) 2
c) 3
d) 4
View Answer
Answer: c
Explanation: The three main issues are affected in design of a learning element are components, feedback and representation.

4. What is used in determining the nature of the learning problem?
a) Environment
b) Feedback
c) Problem
d) All of the mentioned
View Answer
Answer: b
Explanation: The type of feedback is used in determining the nature of the learning problem that the agent faces.

5. How many types are available in machine learning?
a) 1
b) 2
c) 3
d) 4
View Answer
Answer: c
Explanation: The three types of machine learning are supervised, unsupervised and reinforcement.

6. Which is used for utility functions in game playing algorithm?
a) Linear polynomial
b) Weighted polynomial
c) Polynomial
d) Linear weighted polynomial
View Answer
Answer: d
Explanation: Linear weighted polynomial is used for learning element in the game playing programs.

7. Which is used to choose among multiple consistent hypotheses?
a) Razor
b) Ockham razor
c) Learning element
d) None of the mentioned
View Answer
Answer: b
Explanation: Ockham razor prefers the simplest hypothesis consistent with the data intuitively.

8. What will happen if the hypothesis space contains the true function?
a) Realizable
b) Unrealizable
c) Both Realizable & Unrealizable
d) None of the mentioned
View Answer
Answer: b
Explanation: A learning problem is realizable if the hypothesis space contains the true function.

9. What takes input as an object described by a set of attributes?
a) Tree
b) Graph
c) Decision graph
d) Decision tree
View Answer
Answer: d
Explanation: Decision tree takes input as an object described by a set of attributes and returns a decision.

10. How the decision tree reaches its decision?
a) Single test
b) Two test
c) Sequence of test
d) No test
View Answer
Answer: c
Explanation: A decision tree reaches its decision by performing a sequence of tests.

Learning – 2

1. Factors which affect the performance of learner system does not include?
a) Representation scheme used
b) Training scenario
c) Type of feedback
d) Good data structures
View Answer
Answer: d
Explanation: Factors which affect the performance of learner system does not include good data structures.

2. Which of the following does not include different learning methods?
a) Memorization
b) Analogy
c) Deduction
d) Introduction
View Answer
Answer: d
Explanation: Different learning methods include memorization, analogy and deduction.

3. Which of the following is the model used for learning?
a) Decision trees
b) Neural networks
c) Propositional and FOL rules
d) All of the mentioned
View Answer
Answer: d
Explanation: Decision trees, Neural networks, Propositional rules and FOL rules all are the models o learning.

4. Automated vehicle is an example of ______
a) Supervised learning
b) Unsupervised learning
c) Active learning
d) Reinforcement learning
View Answer
Answer: a
Explanation: In automatic vehicle set of vision inputs and corresponding actions are available to learner hence it’s an example of supervised learning.

5. Which of the following is an example of active learning?
a) News Recommender system
b) Dust cleaning machine
c) Automated vehicle
d) None of the mentioned
View Answer
Answer: a
Explanation: In active learning, not only the teacher is available but the learner can ask suitable perception-action pair examples to improve performance.

6. In which of the following learning the teacher returns reward and punishment to learner?
a) Active learning
b) Reinforcement learning
c) Supervised learning
d) Unsupervised learning
View Answer
Answer: b
Explanation: Reinforcement learning is the type of learning in which teacher returns reward or punishment to learner.

7. Decision trees are appropriate for the problems where ___________
a) Attributes are both numeric and nominal
b) Target function takes on a discrete number of values.
c) Data may have errors
d) All of the mentioned
View Answer
Answer: d
Explanation: Decision trees can be used in all the conditions stated.

8. Which of the following is not an application of learning?
a) Data mining
b) WWW
c) Speech recognition
d) None of the mentioned
View Answer
Answer: d
Explanation: All mentioned options are applications of learning.

9. Which of the following is the component of learning system?
a) Goal
b) Model
c) Learning rules
d) All of the mentioned
View Answer
Answer: d
Explanation: Goal, model, learning rules and experience are the components of learning system.

10. Which of the following is also called as exploratory learning?
a) Supervised learning
b) Active learning
c) Unsupervised learning
d) Reinforcement learning
View Answer
Answer: c
Explanation: In unsupervised learning, no teacher is available hence it is also called unsupervised learning

Learning – 3

1. Which is not a desirable property of a logical rule-based system?
a) Locality
b) Attachment
c) Detachment
d) Truth-Functionality
View Answer
Answer: b
Explanation: Locality: In logical systems, whenever we have a rule of the form A => B, we can conclude B, given evidence A, without worrying about any other rules. Detachment: Once a logical proof is found for a proposition B, the proposition can be used regardless of how it was derived .That is, it can be detachment from its justification. Truth-functionality: In logic, the truth of complex sentences can be computed from the truth of the components. However, there are no Attachment properties lies in a Rule-based system. Global attribute defines a particular problem space as user specific and changes according to user’s plan to problem.

2. How is Fuzzy Logic different from conventional control methods?
a) IF and THEN Approach
b) FOR Approach
c) WHILE Approach
d) DO Approach
View Answer
Answer: a
Explanation: FL incorporates a simple, rule-based IF X AND Y THEN Z approach to a solving control problem rather than attempting to model a system mathematically.

3. 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
View Answer
Answer: b
Explanation: The problem of unsupervised learning involves learning patterns in the input when no specific output values are supplied. We cannot expect the specific output to test your result. Here the agent does not know what to do, as he is not aware of the fact what propose system will come out. We can say an ambiguous un-proposed situation.

4. Inductive learning involves finding a __________
a) Consistent Hypothesis
b) Inconsistent Hypothesis
c) Regular Hypothesis
d) Irregular Hypothesis
View Answer
Answer: a
Explanation: Inductive learning involves finding a consistent hypothesis that agrees with examples. The difficulty of the task depends on the chosen representation.

5. Computational learning theory analyzes the sample complexity and computational complexity of __________
a) Unsupervised Learning
b) Inductive learning
c) Forced based learning
d) Weak learning
View Answer
Answer: b
Explanation: Computational learning theory analyzes the sample complexity and computational complexity of inductive learning. There is a tradeoff between the expressiveness of the hypothesis language and the ease of learning.

6. 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
View Answer
Answer: c
Explanation: Consistent hypothesis go with examples, If the hypothesis says it should be negative but infect it is positive, it is false negative. If a hypothesis says it should be positive, but in fact, it is negative, it is false positive. In a specialized hypothesis we need to have certain restrict or special conditions.

7. Neural Networks are complex ______________with many parameters.
a) Linear Functions
b) Nonlinear Functions
c) Discrete Functions
d) Exponential Functions
View Answer
Answer: b
Explanation: Neural networks parameters can be learned from noisy data and they have been used for thousands of applications, so it varies from problem to problem and thus use nonlinear functions.

8. A perceptron is a ______________
a) Feed-forward neural network
b) Backpropagation algorithm
c) Backtracking algorithm
d) Feed Forward-backward algorithm
View Answer
Answer: a
Explanation: A perceptron is a Feed-forward neural network with no hidden units that can be representing only linear separable functions. If the data are linearly separable, a simple weight updated rule can be used to fit the data exactly.

9. 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
View Answer
Answer: a
Explanation: Not all formal languages are context-free.

10. 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
View Answer
Answer: d
Explanation: The union and concatenation of two context-free languages are context-free; but intersection need not be.