Artificial Intelligence MCQ with answers-03

Q:1. What is an advantage of Artificial Intelligence?

1.Potential for misuse
2.Highly dependent on machines
3.Requires Supervision
4.Rational Decision Maker

Solution- 4- Rational Decision Maker

Reason 1- This is because Rational choice theory in A.I. states that it rely on rational calculations to make rational choices that result in outcomes aligned with their own best interests.


Reason 2- This is because Rational choice theory during a .I. states that it believe rational calculations to form rational choices that end in outcomes aligned with their own best interests.


Reason 3- This is because Rational choice theory during artificial intelligence states that it believe rational calculations to make rational choices that end in outcomes aligned with their own best interests.


Q:2. Who is known as the “Father of AI”?

1.Fisher Ada
2.Alan Turing
3.John McCarthy
4.Allen Newell

Solution- 3.John McCarthy

Reason 1- John McCarthy, widely recognized as the father of Artificial Intelligence due to his astounding contribution in the field of Computer Science and AI.


Reason 2- John McCarthy, widely known because the father of AI thanks to his astounding contribution within the field of computing and AI.


Reason 3- John McCarthy, widely known because the father of Artificial intelligence because of his astounding contribution within the sector of computing and AI.


Q:3. Which of the following is not a branch of Artificial Intelligence?

1.Expert systems
2.Robotics
3.Natural language Processing
4.None of the above

Solution- 4.None of the above

Reason 1- This is because Expert System, Robotics and Natural Language processing all have their roles in Artificial Intelligence. There are 6 branches of A.I and these 3 above come under these branches.


Reason 2- This is because Expert System, Robotics and NLP all have their roles in AI . There are 6 branches of A.I and these 3 above come under these branches.


Reason 3- This is because Expert System, Robotics and NLP processing have their roles in AI . There are six branches


Q:4. Which of the following is not an application of Unsupervised Learning?

1. Document clustering
2. Speech recognition
3. Image compression

4. Association analysis

Solution- 4. Speech Recognition

Reason 1- This is the kind of application where you teach the algorithm about your voice and it will be able to recognize you. The most well-known real-world applications are virtual assistants such as Google Assistant and Siri, which will wake up to the keyword with your voice only. That is the reason it comes under supervised learning.


Reason 2-This is the type of application where you teach the algorithm about your voice and it’ll be ready to recognize you. the foremost well-known real-world applications are virtual assistants like Google Assistant and Siri, which can awaken to the keyword together with your voice only. that’s the rationale it comes under supervised learning.


Reason 3-This is the type of application where you teach the algorithm about your voice and it’ll be ready to recognize you. the foremost well-known real-world applications are virtual assistants like Google Assistant and Siri, which can awaken to the keyword in conjunction with your voice only. that’s the rationale it comes under supervised learning.


Q:5. The multi-armed bandit problem is a generalized use case for-

1.Reinforcement learning
2.Supervised learning
3.Unsupervised learning
4.All the above

Solution- 1.Reinforcement learning

Reason 1- Multi–Arm Bandit is a classic reinforcement learning problem, in which a player is facing with k slot machines or bandits, each with a different reward distribution, and the player is trying to maximise his cumulative reward based on trials.


Reason 2- Multi-Arm Bandit may be a classic reinforcement learning problem, during which a player is facing with k slot machins or bandits, each with a special reward distribution, and therefore the player is trying to maximise his cumulative reward supported trials.


Reason 3- Multi-Arm Bandit could also be a classic reinforcement learning problem, during which a player is facing with k slot machines or bandits, each with a special reward distribution, and thus the player is trying to maximise his cumulative reward supported trials.

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