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Syllabus

NEC Computer · Practice

Chapters & sets

1. Basic Electrical and Electronics

Basic Electrical

Set 1106

Basic Electronics

Set 171 Set 233

2. Digital Logic and Microprocessor

Digital Logic

Set 150 Set 250 Set 350 Set 450 Set 550 Set 650 Set 749 Set 851 Set 950 Set 1050 Set 1150 Set 1232

Microprocessor

Set 150 Set 250 Set 350 Set 445 Set 550 Set 650 Set 720 Set 850 Set 969

3. Programming Language and Its Applications

Mixed chapter MCQs

Set 150 Set 249 Set 349 Set 451 Set 540

4. Computer Organization and Embedded System

Mixed chapter MCQs

Set 150 Set 249 Set 350 Set 450 Set 550 Set 650 Set 750 Set 849 Set 950 Set 1050 Set 1150

5. Concept of Computer Network and Network Security System

Basic Networking

Set 150 Set 253

Advanced Networking

Set 150 Set 250 Set 350 Set 450 Set 550 Set 655

6. Theory of Computation and Computer Graphics

Theory of Computation

Set 150 Set 250 Set 360

Computer Graphics

Set 150 Set 251 Set 350 Set 450 Set 550

7. Data Structures and Algorithm, Database System and Operating System

8. Software Engineering and Object-Oriented Analysis & Design

Mixed chapter MCQs

Set 150 Set 250 Set 350 Set 450 Set 550 Set 650 Set 750 Set 926

9. Artificial Intelligence and Neural Networks

Mixed chapter MCQs

Set 150 Set 317 Set 450 Set 550 Set 650 Set 750 Set 850 Set 932

10. Project Planning, Design and Implementation

Engineering Drawing

Set 150 Set 267

Engineering Economics

Set 168

Project Planning & Scheduling

Set 188

Project Mangement

Set 150

Engineering Professional Practice

Set 120

Engineering Regulatory Body

Set 130

Mixed chapter MCQs Set 7

50 questions

Question 1

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

A.
Natural language debugging
B.
Natural language compiling
C.
Natural language understanding
D.
Natural language generation

2

50

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

A.
Natural language debugging
B.
Natural language compiling
C.
Natural language understanding (NLU)
D.
Natural language generation

Question 3

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

A.
Natural language debugging
B.
Natural language compiling
C.
Natural language understanding (NLU)
D.
Natural language generation

Question 4

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

A.
Natural language debugging
B.
Natural language compiling
C.
Natural language understanding (NLU)
D.
Natural language generation

Question 5

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

A.
Natural language debugging and natural language compiling
B.
Natural language publishing and natural language maintenance
C.
Natural language organizing and natural language implementing
D.
Natural language understanding and natural language generation

Question 6

______ are the NLG models and methodologies.

A.
Long-Short term memory
B.
Recurrent Neural Network
C.
Markov chain
D.
All of above

Question 7

NLP is difficult because ______.

A.
Imparting world knowledge is difficult.
B.
Fictitious words
C.
Poorly defined scopes
D.
All of above

Question 8

______ is not the application of NLP.

A.
Opening Computer Browser
B.
Sentiment Analysis
C.
Text Classification
D.
Chat bots and Virtual Assistants

Question 9

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

A.
Computer Application generation
B.
Computer Vision
C.
NLP
D.
None of above

Question 10

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

A.
Computer Application generation
B.
Computer Vision
C.
NLP
D.
NLG

Question 11

Consider the below image and answer the best solution. This figure is the complete process of ______. Input Sensing device Interpreting device Output

A.
Computer Application generation
B.
Computer Vision
C.
NLP
D.
NLG

Question 12

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

A.
Computer Application generation
B.
Computer Vision
C.
NLP
D.
NLG

Question 13

A computer vision technique that relies on image templates is:

A.
Edge detection
B.
Binocular vision
C.
Model-based vision
D.
Robot vision

Question 14

______ 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.

A.
Machine Vision / Computer Vision
B.
Binocular vision
C.
Model-based vision
D.
Robot vision

Question 15

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

A.
Robot
B.
Pattern Recognition
C.
Image Recognition
D.
Agent

Question 16

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

A.
Get information from its surroundings
B.
Physically move or manipulate objects
C.
Robot
D.
Machine
E.
Image Recognizer
F.
Agent

Question 17

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.

A.
Robot
B.
Machine
C.
Image Recognizer
D.
Agent

Question 18

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

A.
Dynamics
B.
Physics
C.
Robotics
D.
Kinematics

Question 19

______ is the Robot control approaches in AI

A.
Reactive control
B.
Pro-active control
C.
Non-reactive control
D.
Formal control

Question 20

______ has the ability to learn without being explicitly programmed.

A.
Application Learning (AL)
B.
Machine Learning (ML)
C.
Neural Network (NN)
D.
Computer Vision (CV)

Question 21

ML is field of AI, consisting of learning algorithms that

A.
Over time with experience
B.
At executing some task
C.
Improve their performance
D.
All of the above

Question 22

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

A.
Machine Learning
B.
Artificial Intelligence
C.
Convolutional Neural Network
D.
Bayes Network

Question 23

______ is one of the forms of machine learning.

A.
Rote learning
B.
Induction learning
C.
Explanation based learning
D.
All of above

Question 24

______ is possible on the basis of memorization.

A.
Rote learning
B.
Induction learning
C.
Explanation based learning
D.
All of above

Question 25

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

A.
Rote learning
B.
Induction learning
C.
Explanation based learning
D.
None of above

Question 26

______ deals with an idea of single-example learning.

A.
Rote learning
B.
Induction learning
C.
Explanation based learning
D.
None of above

Question 27

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

A.
Instance-based, Explanation based
B.
Rote, Explanation
C.
Explanation based, Instance-based
D.
Explanation, Rote

Question 28

learning algorithms are trained using labeled data.

A.
Un-supervised
B.
Reinforcement
C.
Supervised
D.
Semi-supervised

Question 29

learning algorithms are trained using unlabeled data.

A.
Un-supervised
B.
Reinforcement
C.
Supervised
D.
Semi-supervised

Question 30

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

A.
Un-supervised
B.
Reinforcement
C.
Supervised
D.
Semi-supervised

Question 31

learning model does not take any feedback.

A.
Un-supervised
B.
Reinforcement
C.
Supervised
D.
Semi-supervised

Question 32

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

A.
20:80
B.
80:20
C.
60:40
D.
40:60

Question 33

______ are the two types of Supervised learning.

A.
Classification and Regression
B.
Clustering and Association
C.
Classification and Association
D.
Clustering and Regression

Question 34

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

A.
Classification
B.
Regression
C.
Clustering
D.
Association

Question 35

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

A.
Classification
B.
Regression
C.
Clustering
D.
Association

Question 36

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.

A.
Regression
B.
Classification
C.
Association
D.
Clustering

Question 37

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.

A.
Regression
B.
Classification
C.
Association
D.
Clustering

Question 38

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

A.
Regression
B.
Classification
C.
Association
D.
Clustering

Question 39

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

A.
Regression
B.
Classification
C.
Association
D.
Clustering

Question 40

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

A.
Un-supervised learning
B.
Supervised learning
C.
Reinforcement learning
D.
Clustering

Question 41

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

A.
Un-supervised learning
B.
Supervised learning
C.
Reinforcement learning
D.
Clustering

Question 42

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

A.
Un-supervised learning
B.
Supervised learning
C.
Reinforcement learning
D.
None of above

Question 43

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

A.
Root structure
B.
Forest structure
C.
Tree structure
D.
Node structure

Question 44

______ is one of the types of decision tree.

A.
Categorical variable decision tree
B.
Continuous variable decision tree
C.
Static variable decision tree
D.
Both A and B

Question 45

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.

A.
Maps
B.
Graphs
C.
Decision tree
D.
Artificial NN

Question 46

______ are the decision tree nodes.

A.
End node
B.
Decision node
C.
Chance node
D.
All of above

Question 47

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

A.
Circles
B.
Squares
C.
Triangle
D.
Rectangles

Question 48

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

A.
Circles
B.
Squares
C.
Triangle
D.
Rectangles

Question 49

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

A.
Circles
B.
Squares
C.
Triangle
D.
Rectangles

Question 50

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

A.
Hebbian learning
B.
Bayesian learning
C.
Neural learning
D.
Supervised learning

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