# Artificial Intelligence MCQ Quiz - Objective Question with Answer for Artificial Intelligence - Download Free PDF

Last updated on Nov 23, 2023

## Latest Artificial Intelligence MCQ Objective Questions

#### Artificial Intelligence Question 1:

____ does not serve as an application of AI.

1. Natural language processing
2. Database Management System
3. Digital Assistants
4. Computer Vision

Option 2 : Database Management System

#### Artificial Intelligence Question 1 Detailed Solution

The correct answer is Database Management System Key Points

Natural language processing, Digital Assistants and Computer Vision are application of AI but Database Management System is not an application of AI.

• Digital Assistants: A digital assistant (or virtual assistant) is an application of AI. These systems leverage AI techniques such as natural language processing and speech recognition to understand user queries and provide responses. Examples include Siri, Alexa, and Google Assistant.
• Natural Language Processing (NLP): NLP is an area of AI that focuses on the interactions between computers and humans via natural language. It involves several AI techniques that support understanding, interpretation, and generation of human languages in a valuable way.
• Computer Vision: This is another application of AI. It involves empowering computers to gain high-level understanding from digital images or videos. This field requires AI to interpret and make sense of visual data.
• Database Management System (DBMS): This is not inherently an application of AI. A DBMS is used primarily for storing, retrieving, and managing large amounts of data. While advanced DBMS may incorporate AI techniques for tasks such as optimizing queries, the basic functions of a DBMS do not require artificial intelligence.

#### Artificial Intelligence Question 2:

Which one of the example of active learning:

1. Dust cleaning machine
2. Automated vehicle
3.  News recommendation system
4. None of the above

Option 3 :  News recommendation system

#### Artificial Intelligence Question 2 Detailed Solution

The correct answer is ​ News recommendation system Key Points

• Active learning is a type of machine learning where a model can query a user (or some other source of information) to acquire new data upon which it can be trained.
• A news recommendation system is an application where active learning can be employed, as the system can learn from user feedback, ratings, or interaction with news articles to improve its performance over time.
• In contrast, a dust cleaning machine or automated vehicle may use machine learning, but are typically not examples of active learning unless explicitly designed with the capacity to consistently request and assimilate new information for model improvement.

#### Artificial Intelligence Question 3:

Disha has showcased certain Neural Network-related features for her project. Identify the features she has presented inaccurately.
(i) Neural Network systems are modelled on human brain and nervous system.
(ii) They are able to extract features with the help of inputs provided by the programmer.
(iii) Every Neural Network node is essentially a machine learning algorithm.
(iv) It is useful when solving problems for which the data set is very small.

1. (ii) and (iv)
2. (i) and (ii)
3. Only (ii)
4. (iii) and (iv)
5. None of the above

Option 1 : (ii) and (iv)

#### Artificial Intelligence Question 3 Detailed Solution

The correct answer is (ii) and (iv) Key Points

1. (ii) They are able to extract features with the help of inputs provided by the programmer - This is not entirely correct. While the programmer provides the inputs, neural networks are predominantly known for their ability to automatically learn and extract the important features from the input data during the training process. This is a characteristic feature of deep learning models, which falls under the broader umbrella of neural networks. However, it's true that for some specific types of neural networks (like Convolutional Neural Networks), the programmer designs the network in such a way that it can effectively learn spatial hierarchies within an image.
2. (iv) It is useful when solving problems for which the data set is very small - This is generally untrue. Neural networks typically require a large amount of data to learn effectively. For small data sets, simpler machine learning algorithms, such as decision trees or Naïve Bayes, may be more effective because they are less likely to overfit the limited data available, unlike complex models like neural networks which tend to perform best with larger datasets. Additional Information

1. (i) Neural Network systems are modelled on human brain and nervous system - This is true. Artificial Neural Networks (ANNs) are designed inspired by the human brain's biological neural networks. They consist of interconnected layers of nodes, or "artificial neurons," that simulate the neurons in the human brain. Just like in the human brain, where signals are passed from neuron to neuron, the artificial neurons process inputs and pass on the transformed information.
2. Every Neural Network node is essentially a machine learning algorithm - While neural network nodes (neurons) perform computations, they are machine learning algorithms. The learning in a neural network occurs through the optimization of weights connecting the nodes.

#### Artificial Intelligence Question 4:

The learning model in question exclusively utilizes dynamic environmental inputs, absent any labeled data. This behavioural learning system enhances its proficiency as it gains knowledge about the environment. During its interaction with the environment, the learning agent, transitioning from one state to another, experiences rewards for successful actions and penalties for failures.

1. Unsupervised Learning
2. Reinforcement Learning
3. Supervised Learning
4. All of the above
5. None of the above

Option 2 : Reinforcement Learning

#### Artificial Intelligence Question 4 Detailed Solution

The correct answer is Reinforcement Learning Key Points

• Reinforcement Learning: This is a form of machine learning where an agent learns to make decisions by performing certain actions in an environment to achieve a goal.
• The agent is not provided with what action to take but instead must discover which actions yield the most reward, often through trial and error.
• The agent is given rewards or penalties for the actions it performs, thus enabling it to adjust its behavior to maximize the reward over time.
• Reinforcement learning algorithms include Q-Learning, Deep Q Network, Policy Gradient methods, etc. Additional Information

• Supervised Learning: In this type of learning, an AI is given a dataset that is already labeled. This means the AI is provided with both the input data and the corresponding correct output. The AI then creates a model that maps the inputs to the outputs. This model can further be used to predict the output for new/unseen inputs. Examples include algorithms for classification, regression, and prediction tasks, like SVM, Decision Trees, Random Forest, and Neural Networks, among others.
• Unsupervised Learning: Here, there are no labels provided, so the AI must find structure in the input all by itself. The goal is to model the underlying structure or distribution in the data in order to learn more about the data. This is typically used for clustering, dimensionality reduction, and association rules among others. Examples include K-means, hierarchical clustering, PCA, etc.

#### Artificial Intelligence Question 5:

Choose the incorrect statement(s) from the following:
(i) Deep learning is a subset of Machine Learning
(ii) Machine Learning is a subset of Deep Learning
(iii) Artificial Intelligence is a subset of Deep Learning
(iv) Deep Learning is the advanced form of AI and ML

1. Only (i)
2. (ii) and (iii)
3. (i) and (ii)
4. Only (iii)
5. Only (iv)

Option 2 : (ii) and (iii)

#### Artificial Intelligence Question 5 Detailed Solution

The correct answer is (ii) and (iii) Key Points

1. Deep learning is a subset of Machine Learning - This is correct.
2. Machine Learning is a subset of Deep Learning - This is incorrect. The relationship is actually the reverse - Deep Learning is a subset of Machine Learning.
3. Artificial Intelligence is a subset of Deep Learning - This is also incorrect. Again, the relationship is the opposite - Deep Learning is a subset of AI.
4. Deep Learning is the advanced form of AI and ML - This is correct. Deep learning is an advanced form of machine learning/ Subset of AI and ML. ## Top Artificial Intelligence MCQ Objective Questions

#### Artificial Intelligence Question 6

In Artificial intelligence (AI) which agent deals with happy and unhappy state?

1. Utility based agent
2. Model based agent
3. Simple reflex agent
4. Learning agent

Option 1 : Utility based agent

#### Artificial Intelligence Question 6 Detailed Solution

The correct answer is "option 1".

CONCEPT:

According to computer science, Artificial Intelligence is the study of "intelligent agents" or "rational agents and their environment".

Agent: Agent is those that work or act in their environment.

Environment: It is the surrounding of an agent.

An agent takes input from the environment through sensors & delivers the output to the environment through actuators.

EXPLANATION:

option1: Simple reflex agent acts on the basis of current percept and ignores percept history i.e. all the history an agent has perceived till date.

option2: Model-based agents have a model i.e. knowledge about how things happen in the whole world & based on the model it performs actions.

option3: Learning agents learn from past experiences and automatically adapt through learning. It has learning capabilities.

option4: Utility-based agents choose actions on the basis of utilities or preference and chooses the actions that maximize the expected utility. Utility describes the happiness of the agent.

Hence, Utility-based agents deal with happy & unhappy agents. Additional Information

1. Sensors are used to detect any type of change in the environment & inform other electronic devices.

2. Actuators convert energy into motion & are responsible for the movement of the system.

3Rational agent acts in a way that improves the performance measure with any possible actions.

#### Artificial Intelligence Question 7

Which of the following is not a source of Artificial Intelligence bias?

1. Data
2. Algorithms
3. People
4. Ability

Option 4 : Ability

#### Artificial Intelligence Question 7 Detailed Solution Key PointsSources of Artificial intelligence Bias:

• Data:
• This is often the primary source of bias in AI.
• If the training data used to build a model includes biases, those biases will be learned by the model.
• For instance, if a facial recognition algorithm is trained mostly on images of light-skinned people, it might perform poorly when trying to recognize darker-skinned people.
• Algorithms:
• Although algorithms are mathematical and should be objective, bias can creep in through the assumptions and decisions made during their design.
• For example, if an algorithm is designed to maximize a certain metric without considering fairness, it may lead to biased outcomes.
• People:
• People who design, train, and implement AI systems can introduce bias, often unconsciously.
• This includes the people who decide what data to collect and use, the designers of the algorithms, and the decision-makers who apply the AI's outputs.
• Ability:
• This refers to an individual's capacity to perform tasks. While it might affect the use and interpretation of AI, it's not a direct source of bias within AI systems themselves.
• AI doesn't "learn" from people's abilities in the same way it learns from biased data or biased decisions made during the design process.
• That said, if AI tools are only accessible or usable by a particular group of people due to varying abilities, this could lead to systemic biases in a broader sense.

#### Artificial Intelligence Question 8

Consider the following

(a) Trapping at local maxima

(b) Reaching a plateau

(c) Traversal along the ridge

Which of the following option represents shortcomings of the hill climbing algorithm?

1. (a) and (b) only
2. (a) and (c) only
3. (b) and (c) only
4. (a), (b) and (c)

Option 4 : (a), (b) and (c)

#### Artificial Intelligence Question 8 Detailed Solution

Concept:

In hill climbing algorithm, at each point in the search path, a successor node that appears to lead most quickly to the top of the hill or goal is selected for exploration.

In this method of searching, the generate and test method is augmented by a heuristic foundation which measures the closeness of the current state to the goal state.

Explanation:

Problems in hill climbing are:

1) Local maximum: It is a state which is better than all of its neighbours but is not better than some other states which are farther away. Solution of this problem is backtracking.

2) Plateau: It is a flat area of search space in which a whole set of neighbouring states have the same value. On a plateau, it is not possible to determine the best direction in which to move.

3) Ridge:  It is an area of search space which is higher than the surrounding areas and that itself has a slope.

#### Artificial Intelligence Question 9

Who is known as father of Artificial Intelligence?

1. Alan Turing
2. John McCarthy
3. Blaise Pascal
4. Dartmouth

Option 2 : John McCarthy

#### Artificial Intelligence Question 9 Detailed Solution

The correct answer is John McCarthy Key PointsJohn McCarthy:

• The title "Father of Artificial Intelligence" is often given to Dr. John McCarthy.
• McCarthy was born in Boston, Massachusetts, USA in 1927 and passed away in 2011.
• He coined the term "Artificial Intelligence" in 1956 while proposing a conference at Dartmouth College.
• His groundbreaking work laid the foundations for AI as a distinct field within computer science.
• McCarthy was one of the co-creators of the Lisp programming language, instrumental in AI development.
• He also initiated the concept of "time-sharing" systems, enhancing computer efficiency.
• His concept of the "Advice Taker" in 1958 can be seen as the forerunner to modern knowledge-based systems.
• McCarthy championed the development of Logic programming, a crucial theory for AI.
• His vision included machines that could carry on meaningful conversations with humans, a key tenet of modern AI.
• He was awarded the Turing Award in 1971, the highest honor in computer science, for his contributions to AI.

#### Artificial Intelligence Question 10

Let the population of chromosomes in genetic algorithm is represented in terms of binary number. The strength of fitness of a chromosome in decimal form, x, is given by

$$Sf\left( x \right) = \frac{{f\left( x \right)}}{{\sum f\left( x \right)}}\;where\;\;f\left( x \right) = {x^2}$$

The population is given by P where:

P = {(01101), (11000), (01000), (10011)}

The strength of fitness of chromosome (11000) is ______

1. 24
2. 576
3. 14.4
4. 49.2

Option 4 : 49.2

#### Artificial Intelligence Question 10 Detailed Solution

P = {(01101), (11000), (01000), (10011)}

Also, strength of fitness of a chromosome in decimal form, x is given by:

$$Sf\left( x \right) = \frac{{f\left( x \right)}}{{\sum f\left( x \right)}}\;where\;\;f\left( x \right) = {x^2}$$

So, first convert the population in decimal form, then find f(x).

 P Value in decimal f(x) = x2 01101 13 169 11000 24 576 01000 8 64 10011 19 361

We have to find the strength of fitness of chromosome 11000.

11000 in decimal = 24, f(24) = 576

$$Sf\left( x \right) = \frac{{576}}{{169\; + \;576\; + \;64\; + \;361}}$$

Sf(x) = 576/1170

Sf(x) = 0.492

#### Artificial Intelligence Question 11

Consider the following terminology and match List 1 and List 2 and choose the correct answer from the code given below

b = branch factor

d = depth of shallowest solution

M = Maximum depth of the search tree

I = depth limit

 List 1 List 2 a) BFS i) O(bd) b) DFS ii) O(bd) c) Depth – Limited Search iii) O(bm) d) Iterative deepening Search iv) O(bl)

1. a-iii, b-ii, c-iv, d-i
2. a-ii, b-iii, c-iv, d-i
3. a-i, b-ii, c-iv, d-iii
4. a-i, b-iii, c-iv, d-ii

Option 2 : a-ii, b-iii, c-iv, d-i

#### Artificial Intelligence Question 11 Detailed Solution

BFS:

Breadth first search in artificial intelligence is implemented as FIFO. Path is find out by considering the number of edges in that path. Start from the root node, it considers all the nodes. But at the end, selects the path having lowest number of edges. If b is the branching factor and d is the depth. Then overall cost of BFS is O(bd).

DFS:

It is based on the concept of LIFO as that of stack procedure. It considers the nodes one by one from root node to the leaf as a single path. If it does not terminate it can go to the infinity. If b is the branching factor and m is the depth. Then cost of DFS is O(bm).

Depth – Limited Search

In this, depth is specified first, and all the nodes are traversed till that depth then it goes on to next level. If b is the branching factor. Then overall cost of depth limited search is O(bl).

Iterative – deepening search:

It starts from root node i.e. depth 1, expands all the nodes and then goes on to the depth 2, expand all the nodes of that depth. In this way, it expands all the nodes. If b is the branching factor and d is the depth, then overall cost is O(bd).

#### Artificial Intelligence Question 12

Which agent deals with the happy and unhappy state?

1. Utility‐based agent
2. Model‐based agent
3. Goal‐based Agent
4. Learning Agent

Option 1 : Utility‐based agent

#### Artificial Intelligence Question 12 Detailed Solution

The correct answer is option 1.

Artificial Intelligence system is the composed of agent and its environment. Agent takes input from its Environment through sensor and sends its reaction to environment through actuator. Simple Reflex agent : take decisions on the basis of the current environment and ignore the history.

Model-based reflex agent : work partially in the observable environment, and track the situation.

Goal-based agents :  needs to know its goal that describes advisable situations.

Utility-based agents : acts based not only goal but also the best possible way to achieve it.

Learning Agents : learn from its past experiences, or it has learning capabilities.

#### Artificial Intelligence Question 13

A fuzzy conjunction operators, t(x, y), and a fuzzy disjunction operator, s(x, y), form a pair if they satisfy:

t(x, y) = 1 – s(1 – x, 1 - y).

If $$t\left( {x,\;y} \right) = \frac{{xy}}{{\left( {x + y - xy} \right)}}$$ then s(x, y) is given by

1. $$\frac{{x\; + \;y}}{{1\; -\; xy}}$$
2. $$\frac{{x\; + \;y\; - \;2xy}}{{1\; - \;xy}}$$
3. $$\frac{{x\; + \;y\; - \;xy}}{{1\; - \;xy}}$$
4. $$\frac{{x\; + \;y\; - \;xy}}{{1\; + \;xy}}$$

Option 2 : $$\frac{{x\; + \;y\; - \;2xy}}{{1\; - \;xy}}$$

#### Artificial Intelligence Question 13 Detailed Solution

Given:

t(x, y) = 1 – s(1 – x, 1 - y).

$$t\left( {x,\;y} \right) = \frac{{xy}}{{\left( {x\; + \;y\; - \;xy} \right)}}$$

$$s\left( {x,\;y} \right) = 1 - \frac{{\left( {1\; - \;x} \right)\left( {1\; - \;y} \right)}}{{1\; - x\; + \;1\; - \;y\; - \;\left( {1 - x} \right)\left( {1\; - \;y} \right)}}$$

$$s\left( {x,\;y} \right) = 1 - \frac{{1\; - \;y\; - \;x\; + \;xy}}{{1 \;-\; x \;+\; 1\; - \;y\; - \;1 \;+ \;y \;+ \;x\; - \;xy}}$$

$$s\left( {x,\;y} \right) = 1 - \frac{{1\; - \;y\; - \;x\; +\; xy}}{{1\; - \;xy}}$$

$$s\left( {x,\;y} \right) = \frac{{1\; - \;xy\; - \;1\; + \;y\; + \;x\; - \;xy}}{{1 \;- \;xy}}$$

$$s\left( {x,\;y} \right) = \frac{{x\; + \;y \;- \;2xy}}{{1\; -\; xy}}$$

#### Artificial Intelligence Question 14

Match List I with List II

 List I List II (A) Branch-and-bound (I) Keeps track of all partial paths which can be candidate for further exploration. (B) Steepest-accent hill climbing (II) Detects difference between current state and goal state (C) Constraint satisfaction (III) Discovers problem state(s) that satisfy a set of constraints. (D) Means-end-analysis (IV) Considers all moves from current state and selects best move

Choose the correct answer from the options given below:

1. A - I, B - IV, C - III, D - II
2. A - I, B - II, C - III, D - IV
3. A - II, B - I, C - III, D - IV
4. A - II, B - IV, C - III, D - I

Option 1 : A - I, B - IV, C - III, D - II

#### Artificial Intelligence Question 14 Detailed Solution

The correct answer is option 1. Key Points

Branch-and-bound:

Keeps track of all partial paths which can be a candidate for further exploration. (ex: A* Search, BFS)

Steepest-accent hill climbing:

Considers all moves from the current state and selects the best move. (ex:hill-climbing algorithm. )

Constraint satisfaction:

Discovers problem state(s) that satisfy a set of constraints. (ex: Eight queens puzzle. Map coloring problem.)

Means-end-analysis:

Detects the difference between the current state and the goal state. (ex: Means-end-analysis)

∴ Hence the correct answer is A - I, B - IV, C - III, D - II.

#### Artificial Intelligence Question 15

Given below are two statements:

Statement I: A genetic algorithm is a stochastic hill-climbing search in which a large population of states is maintained.

Statement II: In nondeterministic environments, agents can apply AND-OR search to generate contingent plans that reach the goal regardless of which outcomes occur during execution.

In the light of the above statements, choose the Correct answer from the options given below

1. Both Statement I and Statement II are true
2. Both Statement I and Statement II are false
3. Statement I is correct but Statement II is false
4. Statement I is incorrect but Statement II is true

Option 1 : Both Statement I and Statement II are true

#### Artificial Intelligence Question 15 Detailed Solution

The correct answer is option 1. Key Points

A genetic algorithm is a stochastic hill-climbing algorithm that maintains a wide population of states. Mutation and crossover, which blends pairs of states from the population, create new states.

Hence Statement I is correct.

Agents may use AND-OR search in non-deterministic environments to produce contingent plans that achieve the target regardless of which outcomes occur during execution.

Hence Statement II is correct Additional Information

• AND-OR search trees:
• ​OR nodes: The only branching in a deterministic setting is implemented by the agent's own choices in each state; we call these nodes OR nodes.
• AND nodes: Branching is often implemented in a non-deterministic setting by the environment's choice of outcome for each action, which we call nodes AND nodes.
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