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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 9

32 questions

Question 1

______ is a possible mechanism for synaptic modification in the brain.

A.
Hebbian Rule
B.
McCulloch pits neuron
C.
Hopfield network
D.
None of above

Question 2

50

The ______ can be used to train neural networks for pattern recognition.

A.
Hebbian Rule
B.
McCulloch pits neuron
C.
Hopfield network
D.
Genetic Algorithm

Question 3

If two neurons on either side of a synapse are activated simultaneously, the strength of the synapse will increase is ______.

A.
Hebbian Learning Rule
B.
Perceptron Learning Rule
C.
Delta Learning Rule
D.
Genetic Algorithm Learning Rule

Question 4

______ is the one of the Neural Network Learning rules.

A.
Hebbian Learning Rule
B.
Perceptron Learning Rule
C.
Delta Learning Rule
D.
All of above

Question 5

______ is an error correcting the supervised learning algorithm of single layer feedforward networks with linear activation function.

A.
Hebbian Learning Rule
B.
Perceptron Learning Rule
C.
Delta Learning Rule
D.
None of above

Question 6

______ also called Least Mean Square (LMS)

A.
Hebbian Learning Rule
B.
Perceptron Learning Rule
C.
Delta Learning Rule
D.
None of above

Question 7

A ______ is an algorithm for supervised learning of binary classifiers. This algorithm enables neurons to learn and processes elements in the training set one at a time.

A.
Hebbian Learning
B.
Delta Learning
C.
Perceptron
D.
Delta Learning

Question 8

______ are one of the types of perceptron.

A.
Single layer perceptron
B.
Multilayer perceptron
C.
No layer perceptron
D.
Both A and B

Question 9

______ can learn only linearly separable patterns.

A.
Single layer perceptron
B.
Multilayer perceptron
C.
No layer perceptron
D.
None of above

Question 10

______ can learn about two or more layers having a greater processing power.

A.
Single layer perceptron
B.
Multilayer perceptron
C.
No layer perceptron
D.
None of above

Question 11

The given figure is of ______. \[\begin{array}{c} \text{Xi} \ \text{X2} \ \text{Xn} \end{array}] Input Sum Activation Function

A.
Single layer perceptron
B.
Multilayer perceptron
C.
No layer perceptron
D.
None of above

Question 12

The given figure is of ______. Input layer Hidden layers Output layer

A.
Single layer perceptron
B.
Multilayer perceptron
C.
No layer perceptron
D.
None of above

Question 13

Identify the type of perceptron activation function.

y = \frac{1}{1 + e^{-x}}

\

y = \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}

A.
Sigmoid and tanh
B.
Tanh and sigmoid
C.
Linear, Relu
D.
Relu, Linear

Question 14

Identify the type of perceptron activation function.

y = \max(0, x)

\

y = \max(\alpha x, x)

A.
Sigmoid and tanh
B.
Tanh and sigmoid
C.
Leaky Relu and Relu
D.
Relu and, Leaky Relu

Question 15

Gradient descent is an optimization algorithm which is commonly-used to ______ machine learning models and neural networks.

A.
Train
B.
Test
C.
Validate
D.
None of above

Question 16

______ is one of the types of gradient descent algorithm.

A.
Batch Gradient Descent (BGD)
B.
Stochastic Gradient Descent (SGD)
C.
Mini-Batch Gradient Descent
D.
All of above

Question 17

The backpropagation algorithm is used for which type of neural network?

A.
Single-layer feedforward neural networks
B.
Multilayer feedforward neural networks
C.
Convolutional neural networks
D.
Recurrent neural networks

Question 18

Which of the following is true about the backpropagation algorithm?

A.
It is a supervised learning algorithm
B.
It is an unsupervised learning algorithm
C.
It is a reinforcement learning algorithm
D.
It is a semi-supervised learning algorithm

Question 19

The backpropagation algorithm involves two phases. What are they?

A.
Forward propagation and backward propagation
B.
Feature selection and feature extraction
C.
Clustering and classification
D.
Regression and classification

Question 20

Which of the following is the activation function commonly used in the backpropagation algorithm?

A.
Linear
B.
Sigmoid
C.
ReLU
D.
Tanh

Question 21

The backpropagation law is also known as generalized delta rule, is it true?

A.
Yes
B.
No
C.
Partially yes
D.
Not sure

Question 22

______ consists of a set of neurons where each neuron corresponds to a pixel of the difference image and is connected to all the neurons in the neighborhood.

A.
Hopfield neural network
B.
Biological neural network
C.
Hamming neural network
D.
McColloch Pits' neural network

Question 23

The ______ is commonly used for auto-association and optimization tasks.

A.
Hopfield neural network
B.
Biological neural network
C.
Hamming neural network
D.
McColloch Pits' neural network

Question 24

In ______, the input and output patterns are discrete vector, which can be either binary 0,1 or bipolar +1, -1 in nature.

A.
Continuous Hopfield n/w
B.
Discrete Hopfield n/w
C.
Sequential Hopfield n/w
D.
None of above

Question 25

Continuous Hopfield Network in comparison with Discrete Hopfield network, continuous network has ______ as a continuous variable.

A.
Space
B.
Range
C.
Time
D.
Velocity

Question 26

______ architecture can be build up by adding electrical components such as amplifiers which can map the input voltage to the output voltage over a sigmoid activation function.

A.
Continuous Hopfield n/w
B.
Discrete Hopfield n/w
C.
Sequential Hopfield n/w
D.
None of above

Question 27

______, is a network having a single linear unit.

A.
Madeline
B.
Adaline
C.
Backpropagation
D.
Perceptron

Question 28

The basic structure of ______ is similar to perceptron having an extra feedback loop.

A.
Madeline
B.
Adaline
C.
Backpropagation
D.
None of above

Question 29

Identify the diagram and answer the question which training algorithm is this?

A.
Madeline
B.
Adaline
C.
Perceptron
D.
Backpropagation

Question 30

______ is a network which consists of many Adalines in parallel.

A.
Madeline
B.
Adaline
C.
Backpropagation
D.
Perceptron

Question 31

In NN, Delta rule works only for the ______.

A.
Hidden layer
B.
Input layer
C.
Weight and bias
D.
Output layer

Question 32

Generalized delta rule, also called as ______ rule, is a way of creating the desired values of the hidden layer.

A.
Feed-forward
B.
Backpropagation
C.
Perceptron
D.
Adaline

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