set-7

301. ______ is a subfield of natural language processing (NLP), which involves transforming human language into a machine-readable format.

1. Natural language debugging

2. Natural language compiling

3. Natural language understanding

4. Natural language generation

Show me the answer

Answer: 3. Natural language understanding

Explanation:

• Natural Language Understanding (NLU) is a subfield of NLP that focuses on transforming human language into a format that machines can understand and process.

• It involves tasks like sentiment analysis, intent recognition, and entity extraction.

302. Automatic Ticket Routing, Machine Translation (MT), Automated Reasoning, Automatic Ticket Tagging & Reasoning, Question Answering etc. these are the examples of ______.

1. Natural language debugging

2. Natural language compiling

3. Natural language understanding (NLU)

4. Natural language generation

Show me the answer

Answer: 3. Natural language understanding (NLU)

Explanation:

• Tasks like Automatic Ticket Routing, Machine Translation, and Question Answering are examples of Natural Language Understanding (NLU).

• These tasks require the machine to understand and interpret human language.

303. ______ produces natural written or spoken language from structured and unstructured data.

1. Natural language debugging

2. Natural language compiling

3. Natural language understanding (NLU)

4. Natural language generation

Show me the answer

Answer: 4. Natural language generation

Explanation:

• Natural Language Generation (NLG) is the process of producing natural written or spoken language from structured or unstructured data.

• It is used in applications like chatbots, report generation, and voice assistants.

304. ______ is used for generating the responses of chatbots and voice assistants such as Amazon's Alexa, Google's Assistant and Apple's Siri.

1. Natural language debugging

2. Natural language compiling

3. Natural language understanding (NLU)

4. Natural language generation

Show me the answer

Answer: 4. Natural language generation

Explanation:

• Natural Language Generation (NLG) is used to generate responses for chatbots and voice assistants like Alexa, Google Assistant, and Siri.

• It converts structured data into human-like language.

305. Chatbots and "suggested text" features in email clients, such as Gmail's Smart Compose, are examples of applications that use both ______.

1. Natural language debugging and natural language compiling

2. Natural language publishing and natural language maintenance

3. Natural language organizing and natural language implementing

4. Natural language understanding and natural language generation

Show me the answer

Answer: 4. Natural language understanding and natural language generation

Explanation:

• Chatbots and "suggested text" features use both Natural Language Understanding (NLU) to interpret user input and Natural Language Generation (NLG) to produce responses.

• These technologies work together to enable seamless human-machine interaction.

306. ______ are the NLG models and methodologies.

1. Long-Short term memory

2. Recurrent Neural Network

3. Markov chain

4. All of above

Show me the answer

Answer: 4. All of above

Explanation:

• Long-Short Term Memory (LSTM), Recurrent Neural Networks (RNN), and Markov chains are all models and methodologies used in Natural Language Generation (NLG).

• These models help in generating coherent and contextually relevant text.

307. NLP is difficult because ______.

1. Imparting world knowledge is difficult.

2. Fictitious words

3. Poorly defined scopes

4. All of above

Show me the answer

Answer: 4. All of above

Explanation:

• NLP is challenging because imparting world knowledge to machines is complex.

• Fictitious words and poorly defined scopes add to the difficulty in understanding and generating human language.

308. ______ is not the application of NLP.

1. Opening Computer Browser

2. Sentiment Analysis

3. Text Classification

4. Chat bots and Virtual Assistants

Show me the answer

Answer: 1. Opening Computer Browser

Explanation:

• Sentiment Analysis, Text Classification, and Chatbots are all applications of NLP.

• Opening a computer browser is a system-level task and is not related to NLP.

309. ______ deals with How to design computers that can see (that is understand and interpret information in images/video).

1. Computer Application generation

2. Computer Vision

3. NLP

4. None of above

Show me the answer

Answer: 2. Computer Vision

Explanation:

• Computer Vision is the field of AI that focuses on enabling computers to interpret and understand visual information from images or videos.

• It involves tasks like object detection, image recognition, and video analysis.

310. In. ______ by, applying machine learning models to images, computers can classify objects and respond like unlocking your smartphone when it recognizes your face.

1. Computer Application generation

2. Computer Vision

3. NLP

4. NLG

Show me the answer

Answer: 2. Computer Vision

Explanation:

• Computer Vision uses machine learning models to classify objects in images.

• Applications like facial recognition for unlocking smartphones are examples of computer vision in action.

311. Consider the below image and answer the best solution. This figure is the complete process of ______.

Input Sensing device Interpreting device Output

1. Computer Application generation

2. Computer Vision

3. NLP

4. NLG

Show me the answer

Answer: 2. Computer Vision

Explanation:

• The process described involves input sensing, interpretation, and output, which aligns with the workflow of Computer Vision.

• Computer Vision systems process visual data to produce meaningful outputs.

312. Two key technologies drive ______: a convolutional neural network and deep learning, a type of machine learning.

1. Computer Application generation

2. Computer Vision

3. NLP

4. NLG

Show me the answer

Answer: 2. Computer Vision

Explanation:

• Convolutional Neural Networks (CNNs) and Deep Learning are key technologies that drive Computer Vision.

• These technologies enable computers to analyze and interpret visual data effectively.

313. A computer vision technique that relies on image templates is:

1. Edge detection

2. Binocular vision

3. Model-based vision

4. Robot vision

Show me the answer

Answer: 3. Model-based vision

Explanation:

• Model-based vision is a computer vision technique that uses predefined templates or models to recognize objects in images.

• It compares the input image with stored templates to identify objects.

314. ______ is the use of devices for optical, non-contact sensing to receive and interpret an image of a real scene automatically, in order to obtain information and or control machines or processes.

1. Machine Vision / Computer Vision

2. Binocular vision

3. Model-based vision

4. Robot vision

Show me the answer

Answer: 1. Machine Vision / Computer Vision

Explanation:

• Machine Vision or Computer Vision involves using optical devices to capture and interpret images for information extraction or process control.

• It is widely used in automation and robotics.

315. ______ is a programmable machine that imitates the actions or appearance of an intelligent human.

1. Robot

2. Pattern Recognition

3. Image Recognition

4. Agent

Show me the answer

Answer: 1. Robot

Explanation:

• A robot is a programmable machine designed to imitate human actions or appearance.

• Robots are used in various applications, from manufacturing to healthcare.

316. To qualify as a ____, it should be able to do following works:

1. Get information from its surroundings

2. Physically move or manipulate objects

3. Robot

4. Machine

5. Image Recognizer

6. Agent

Show me the answer

Answer: 1. Robot

Explanation:

• A robot must be able to gather information from its surroundings and physically interact with objects.

• These capabilities distinguish robots from other machines.

317. Following are the tasks that ____ can perform. Soldering wires to semiconductor chips, assembling cookies for Pepperidge, Painting cars at Ford plants, walking into live volcanoes, driving trains in Paris, flying to other planets to explore, Dive into deep water to recover things etc.

1. Robot

2. Machine

3. Image Recognizer

4. Agent

Show me the answer

Answer: 1. Robot

Explanation:

• The tasks described, such as soldering, assembling, painting, and exploring, are performed by robots.

• Robots are versatile machines capable of performing a wide range of tasks.

318. ______ is the study of robots, autonomous embodied systems interacting with the physical world.

1. Dynamics

2. Physics

3. Robotics

4. Kinematics

Show me the answer

Answer: 3. Robotics

Explanation:

• Robotics is the field of study that focuses on the design, construction, and operation of robots.

• It involves the interaction of robots with the physical world.

319. ______ is the Robot control approaches in AI

1. Reactive control

2. Pro-active control

3. Non-reactive control

4. Formal control

Show me the answer

Answer: 1. Reactive control

Explanation:

• Reactive control is a robot control approach where the robot reacts to changes in its environment in real-time.

• This approach is commonly used in AI-driven robotics.

320. ______ has the ability to learn without being explicitly programmed.

1. Application Learning (AL)

2. Machine Learning (ML)

3. Neural Network (NN)

4. Computer Vision (CV)

Show me the answer

Answer: 2. Machine Learning (ML)

Explanation:

• Machine Learning (ML) enables systems to learn from data and improve their performance without being explicitly programmed.

• It is a core component of AI.

321. ML is field of AI, consisting of learning algorithms that

1. Over time with experience

2. At executing some task

3. Improve their performance

4. All of the above

Show me the answer

Answer: 4. All of the above

Explanation:

• Machine Learning (ML) involves algorithms that improve their performance over time with experience.

• These algorithms are designed to execute specific tasks and enhance their accuracy through learning.

322. ______ plays an important role in improving and understanding the efficiency of human learning.

1. Machine Learning

2. Artificial Intelligence

3. Convolutional Neural Network

4. Bayes Network

Show me the answer

Answer: 1. Machine Learning

Explanation:

• Machine Learning (ML) helps in understanding and improving the efficiency of human learning by analyzing patterns in data.

• It provides insights into how humans learn and adapt.

323. ______ is one of the forms of machine learning.

1. Rote learning

2. Induction learning

3. Explanation based learning

4. All of above

Show me the answer

Answer: 4. All of above

Explanation:

• Rote learning, Induction learning, and Explanation-based learning are all forms of machine learning.

• These methods represent different approaches to learning from data.

324. ______ is possible on the basis of memorization.

1. Rote learning

2. Induction learning

3. Explanation based learning

4. All of above

Show me the answer

Answer: 1. Rote learning

Explanation:

• Rote learning is based on memorization, where information is repeated until it is learned.

• It does not involve understanding or reasoning.

325. In ______ process, a general rule is induced by the system from a set of observed instances.

1. Rote learning

2. Induction learning

3. Explanation based learning

4. None of above

Show me the answer

Answer: 2. Induction learning

Explanation:

• Induction learning involves deriving general rules or patterns from specific observed instances.

• It is a key method in machine learning for generalization.

326. ______ deals with an idea of single-example learning.

1. Rote learning

2. Induction learning

3. Explanation based learning

4. None of above

Show me the answer

Answer: 3. Explanation based learning

Explanation:

• Explanation-based learning focuses on learning from a single example by deriving a general rule or explanation.

• It is efficient for learning from limited data.

327. ______ learning is more data-intensive, data-driven while ___ learning is more knowledge-intensive, knowledge-driven.

1. Instance-based, Explanation based

2. Rote, Explanation

3. Explanation based, Instance-based

4. Explanation, Rote

Show me the answer

Answer: 1. Instance-based, Explanation based

Explanation:

• Instance-based learning relies heavily on data and examples.

• Explanation-based learning relies on prior knowledge and reasoning.

328. learning algorithms are trained using labeled data.

1. Un-supervised

2. Reinforcement

3. Supervised

4. Semi-supervised

Show me the answer

Answer: 3. Supervised

Explanation:

• Supervised learning algorithms are trained using labeled data, where the input-output pairs are provided.

• The model learns to map inputs to outputs based on the labeled examples.

329. learning algorithms are trained using unlabeled data.

1. Un-supervised

2. Reinforcement

3. Supervised

4. Semi-supervised

Show me the answer

Answer: 1. Un-supervised

Explanation:

• Unsupervised learning algorithms are trained using unlabeled data.

• The model identifies patterns or structures in the data without explicit guidance.

330. learning model takes direct feedback to check if it is predicting correct output or not.

1. Un-supervised

2. Reinforcement

3. Supervised

4. Semi-supervised

Show me the answer

Answer: 3. Supervised

Explanation:

• Supervised learning models receive direct feedback in the form of labeled data to check the correctness of their predictions.

• This feedback helps the model improve its accuracy.

331. learning model does not take any feedback.

1. Un-supervised

2. Reinforcement

3. Supervised

4. Semi-supervised

Show me the answer

Answer: 1. Un-supervised

Explanation:

• Unsupervised learning models do not receive any feedback or labeled data.

• They rely on identifying patterns or clusters in the data without guidance.

332. While training the supervised model, data is usually split in the ratio of

1. 20:80

2. 80:20

3. 60:40

4. 40:60

Show me the answer

Answer: 2. 80:20

Explanation:

• In supervised learning, the data is typically split into an 80:20 ratio, where 80% is used for training and 20% for testing.

• This split ensures that the model is trained on a sufficient amount of data while leaving enough for evaluation.

333. ______ are the two types of Supervised learning.

1. Classification and Regression

2. Clustering and Association

3. Classification and Association

4. Clustering and Regression

Show me the answer

Answer: 1. Classification and Regression

Explanation:

• Classification and Regression are the two main types of supervised learning.

• Classification predicts discrete labels, while regression predicts continuous values.

334. ______ is a process of finding a function which helps in dividing the dataset into classes based on different parameters.

1. Classification

2. Regression

3. Clustering

4. Association

Show me the answer

Answer: 1. Classification

Explanation:

• Classification is the process of dividing a dataset into classes based on specific parameters.

• It is used to predict discrete labels for data points.

335. ______ is a process of finding the correlations between dependent and independent variables.

1. Classification

2. Regression

3. Clustering

4. Association

Show me the answer

Answer: 2. Regression

Explanation:

• Regression is used to find the relationship between dependent and independent variables.

• It predicts continuous values based on input features.

336. Consider the labelled dataset below. It is a dataset of a shopping store which is useful in predicting whether a customer will purchase a particular product under consideration or not based on his/her gender, age and salary.

User ID	Gender	Age	Salary	Purchased
15624510	Male	19	19000	0
15810944	Male	35	20000	1
15668575	Female	26	43000	0
15603246	Female	27	57000	0
15804002	Male	19	76000	1
15728773	Male	27	58000	1
15598044	Female	27	84000	0
15694829	Female	32	150000	1
15600575	Male	25	33000	1
15727311	Female	35	65000	0
15570769	Female	26	80000	1
15606274	Female	26	52000	0
15746139	Male	20	86000	1
15704987	Male	32	18000	0
15628972	Male	18	82000	0
15697686	Male	29	80000	0
15733883	Male	47	25000	1

Input: Gender, Age, Salary. Output: Purchased i.e., 0 or 1. Now look at the prediction data of “Purchased” column in given table and determine which model is this.

1. Regression

2. Classification

3. Association

4. Clustering

Show me the answer

Answer: 2. Classification

Explanation:

• The output variable "Purchased" is binary (0 or 1), indicating a classification problem.

• The goal is to classify whether a customer will purchase the product or not.

337. Consider the labelled data set below. It is a Meteorological dataset which serves the purpose of predicting wind speed based on different parameters.

Temperature	Pressure	Relative Humidity	Wind Direction	Wind Speed
10.69261758	986.882019	54.1937313	195.7150879	3.278597116
13.59184184	987.8729248	48.0648859	189.2951202	2.909167767
17.70494885	988.1119385	39.11965597	192.9273834	2.973036289
20.95430404	987.8500366	30.66773218	202.0752869	2.965285993
22.92782774	987.2833862	26.06723423	210.6589203	2.798230886
24.04233986	986.2907104	23.46918024	221.1188507	2.627005816
24.41475295	985.2338867	22.25082295	233.7911987	2.448749781
23.93361956	984.8914795	22.35178837	244.3504333	2.454271793
22.68800023	984.8461304	23.7538641	253.0864716	2.418341875
20.56425776	984.8380737	27.07867944	264.5071106	2.318677425
17.76400389	985.4262085	33.54900114	280.7827454	2.343950987
11.25680746	988.9365597	53.74139903	68.15406036	1.650191426
14.37810685	989.6819458	40.70884681	72.62069702	1.553468896
18.45114201	990.2960205	30.85038484	71.70604706	1.005017161
22.54895853	989.9562988	22.81738811	44.66042709	0.284133832
24.23155922	988.796875	19.74790765	318.3214111	0.329656571

Input: Temperature, Pressure, Relative Humidity, Wind Direction. Output: Wind Speed. Now look at the prediction data of “Wind Speed” column in given table and determine which modes is this.

1. Regression

2. Classification

3. Association

4. Clustering

Show me the answer

Answer: 1. Regression

Explanation:

• The output variable "Wind Speed" is continuous, indicating a regression problem.

• The goal is to predict the wind speed based on the input features.

338. ______ is a rule-based ML technique which finds out some very useful relations between parameters of a large data set.

1. Regression

2. Classification

3. Association

4. Clustering

Show me the answer

Answer: 3. Association

Explanation:

• Association is a rule-based machine learning technique used to discover relationships between variables in large datasets.

• It is commonly used in market basket analysis.

339. ______ deals with “how can I group these set of items?”

1. Regression

2. Classification

3. Association

4. Clustering

Show me the answer

Answer: 4. Clustering

Explanation:

• Clustering is used to group similar items together based on their characteristics.

• It is an unsupervised learning technique that identifies patterns in data.

340. In ______, model keeps on increasing its performance using a Reward Feedback to learn the behavior or pattern

1. Un-supervised learning

2. Supervised learning

3. Reinforcement learning

4. Clustering

Show me the answer

Answer: 3. Reinforcement learning

Explanation:

• Reinforcement learning involves an agent that learns by receiving rewards or penalties for its actions.

• The model improves its performance based on the feedback it receives.

341. ______ is a machine learning training method based on rewarding desired behaviors and/or punishing undesired ones.

1. Un-supervised learning

2. Supervised learning

3. Reinforcement learning

4. Clustering

Show me the answer

Answer: 3. Reinforcement learning

Explanation:

• Reinforcement learning uses a reward-punishment mechanism to train models.

• The agent learns to maximize rewards and minimize penalties.

342. Consider an example, how a Robotic dog learns the movement of his arms is an example of ______.

1. Un-supervised learning

2. Supervised learning

3. Reinforcement learning

4. None of above

Show me the answer

Answer: 3. Reinforcement learning

Explanation:

• A robotic dog learning the movement of its arms through trial and error, receiving rewards for correct movements, is an example of reinforcement learning.

• The robot improves its performance based on feedback.

343. Decision tree builds classification or regression models in the form of a ______.

1. Root structure

2. Forest structure

3. Tree structure

4. Node structure

Show me the answer

Answer: 3. Tree structure

Explanation:

• A decision tree builds models in the form of a tree structure, with nodes representing decisions and branches representing outcomes.

• It is used for both classification and regression tasks.

344. ______ is one of the types of decision tree.

1. Categorical variable decision tree

2. Continuous variable decision tree

3. Static variable decision tree

4. Both A and B

Show me the answer

Answer: 4. Both A and B

Explanation:

• Decision trees can handle both categorical and continuous variables.

• They are versatile models that can be used for various types of data.

345. A ______ is a decision support tool that uses a tree like graph of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.

1. Maps

2. Graphs

3. Decision tree

4. Artificial NN

Show me the answer

Answer: 3. Decision tree

Explanation:

• A decision tree is a graphical representation of decisions and their possible consequences.

• It is used for decision-making and predictive modeling.

346. ______ are the decision tree nodes.

1. End node

2. Decision node

3. Chance node

4. All of above

Show me the answer

Answer: 4. All of above

Explanation:

• Decision trees consist of decision nodes, chance nodes, and end nodes.

• These nodes represent different stages in the decision-making process.

347. ______ symbol is used to represent decision node in decision tree.

1. Circles

2. Squares

3. Triangle

4. Rectangles

Show me the answer

Answer: 2. Squares

Explanation:

• In decision trees, squares are used to represent decision nodes.

• These nodes indicate points where a decision must be made.

348. ______ symbol is used to represent chance node in decision tree.

1. Circles

2. Squares

3. Triangle

4. Rectangles

Show me the answer

Answer: 1. Circles

Explanation:

• In decision trees, circles are used to represent chance nodes.

• These nodes indicate points where outcomes are uncertain.

349. ______ symbol is used to represent end nodes in decision tree.

1. Circles

2. Squares

3. Triangle

4. Rectangles

Show me the answer

Answer: 3. Triangle

Explanation:

• In decision trees, triangles are used to represent end nodes.

• These nodes indicate the final outcome or result of a decision path.

350. ______ Simply calculates probability of each hypothesis, given data, and makes predictions based on this.

1. Hebbian learning

2. Bayesian learning

3. Neural learning

4. Supervised learning

Show me the answer

Answer: 2. Bayesian learning

Explanation:

• Bayesian learning involves calculating the probability of each hypothesis given the data and making predictions based on these probabilities.

• It is a probabilistic approach to machine learning.