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Introduction to Computer Science Interview Questions and Answers

So you have finally found your dream job in Computer Science but are wondering how to crack the 2023 Computer Science interview and what could be the probable Computer Science Interview Questions. Every Computer Science interview is different, and the job scope is different too. Keeping this in mind, we have designed the most common Computer Science interview Questions and answers to help you get success in your interview.

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1. What is a file?

A file is a named location that stores data or information permanently. A file is always stored inside a storage device using a file name (e.g., STUDENT.MARKS). A file name typically has a primary and secondary name separated by a “.” (DOT).

2. What is a class?

A class is a blueprint from which objects are created. A class contains methods and variables associated with an instance of a class.

3. What is an object?

}

4. What is a constructor?

A constructor is a method used to create an Object of a class. There are two types of constructor Default & Parameterized constructor.

5. What is the different OOPS principle?

The basic OOPS principle are as follows,

Encapsulation

Abstraction

Inheritance

Polymorphism

6. What is inheritance?

}

7. What is polymorphism?

Polymorphism is the ability of an object to take on multiple forms. Polymorphism is commonly used in OOP when a parent class reference refers to a child class object.

8. What are the instance and class variables?

}

9. Compare the method and constructor?

Method: Used to perform some function or operation.

Method: Has a return type.

10. What is a singleton class? 11. What are the steps for creating the object?

Abc a= new Abc();

12. What is the different type of access modifiers?

• Protected – Visible to package and subclass.

13. Which is the highest operator precedence in Java

The operator with the highest preference is the Postfix operator, i.e. () [].

14. What is an array?

The array is a container with a fixed number of similar data types.

15. What is the difference between equals() and method and == operator?

The equals() is a method that matches the content of the strings, whereas == is an operator and matches the object or reference of the strings.

16. Is string class final?

Yes

17. What is a wrapper class?

To access the primitive data type as an object, we use the wrapper class. They are the following:-

Primitive Type Wrapper class

boolean Boolean

char Character

byte Byte

short Short

int Integer

long Long

float Float

double Double

18. What is the difference between overloading and overriding? 19. What are multiple inheritances in Java?

Java supports multiple inheritances, i.e., the ability of a class to implement more than one Interface. A class can implement multiple Interfaces but cannot extend multiple classes.

20. What is a stream?

Output Stream: Used to write data into a destination.

21. What is a Character stream?

Java Character stream is used to perform input and output for 16-bit Unicode. The main classes users are FileReader and FileWriter, which internally uses FileInputStream and FileOutputStream, so the basic difference is that FileReader and FileWriter read and write two bites at a time, respectively.

22. What is a Byte stream?

The main classes related to byte streams are FileInputStream and FileOutputStream.

23. What is an Interface?

The Interface is a reference type in Java, similar to the class, but it’s a collection of abstract methods. A class can implement multiple interfaces.

24. What is the difference between class and Interface?

Below are the difference between Interface and class:-

The Interface cannot be instantiated.

An interface doesn’t have any constructors.

The Interface only has abstract methods.

A class implements an interface and extends a class.

An interface can extend multiple interfaces.

25. What is an abstract class?

A class that contains the abstract keyword in a declaration is called an abstract class. The properties of the abstract class are as follows:-

Abstract classes may or may not contain abstract methods, but if a class has at least one abstract method, it must be declared abstract.

The abstract class cannot be instantiated.

To use an abstract class, we have to inherit it from another class.

If we inherit an abstract class, we must provide implementations for all its abstract methods.

Basic Interview Questions On Computer Architecture

This article was published as a part of the Data Science Blogathon.

Introduction

this article, we will discuss some questions on Computer Architecture that are important from the perspective of interviews and college examinations.

Computer Architecture means how a computer is interconnected with its hardware components and how they transfer data. Various methods and hardware have increased these computers’ processing speed and efficiency.

History of Computers

1. Charles Babbage: Conceptualization and implementation of the first mechanical computer (1 s 5 tons, and its length is 11 feet

2. The first electronic digital programmable computer, Colossus, was invented in 1943. It uses Vacuum Tubes to process the information.

3. the First programmable electronic computer called ENIAC was invented in 1945. It weighs about 80 tons, and it contains about 18000 vacuum tubes.

Fig. 1 Basic Computer Architecture

Why should we study Computer Architecture?

Below are some reasons why it is beneficial for us to gain knowledge of computer architecture. It helps us to write fast and efficient code. Also, it allows us to identify the proper hardware which fulfills our computational demands.

1. Write better programs

a) Faster

b) Smaller

c) Less power consuming (fewer computations involved)

2. To make suitable design choices for changing needs and evolving technologies

a) GPU

b) Wearable

c) Datacenter

d) Mobile phones

e) Quantum computing etc.

Outcomes of learning Computer Architecture:

1. Understanding of the essential components and the design of a computer,

2. Understanding the functional aspects of different components

3. Identification of the issues involved in the instruction execution

4. Identification and analysis of the issues related to performance improvement

5. Analysis of system requirements

Interview Questions on Computer Architecture

Serial Bus: It is a bus that transfers data bit by bit over a single channel. It periodically transmits data to avoid a collision.

Parallel Bus: It is a bus that parallelly transmits bits over multiple channels. As more than one channel is used, there is no chance of collision.

Serial Bus Parallel Bus

It transfers more bits per I/O cycle.

Less Cost. It transfers data with more speed as compared to the serial bus.

It also has fewer pins, making it less complicated. It has a high update rate, so it could be beneficial where the update rate of bits must be increased.

Serial Bus Parallel Bus

Slower Rate of data transfer. More complexity on the motherboard due to more pins.

Slower bit update rate. Higher Cost

It requires all the bits of the multibyte register, which must be loaded one at a time. It only supports short-distance communication due to cross-talk between parallel lines.

Q2. Explain the differences between Von Neumann and Harvard architectures.

Von Neuman Architecture Harvard Architecture

This same memory is used for storing instructions and data. In this, separate memories are used for storing instructions and data.

Due to the single memory, it has a cheaper cost. Due to multiple memories, it has a higher cost.

The CPU can not simultaneously read and write the instructions and data as a single memory. Due to multiple memories, the CPU can read and write the instructions and data simultaneously.

We can commonly find Von-Neuman Architecture in personnel computers. Harvard Architecture is typical in microcontrollers and signal processing.

Below is the diagrammatic explanation of this two architecture

Fig. 2 Von Neumann V/S Harvard Architecture

The Stored Program Concept is similar to Von-Neuman Architecture. In this, both the instructions and data are stored in the same memory. And before this concept, the instructions and data are stored in two separate memories, entirely different entities (e.g., Harvard Architecture). Below is the figure which shows where this store program concept is used.

It only uses single memory, that’s why its manufacturing cost is significantly less. Also, a single data bus is used to fetch both data and instructions, which reduces its cost furthermore. By using this concept, we can sequentially perform various tasks. Also, it provides high processing power due to only a single memory.

Q4. Explain the role of the instruction set architecture as a layer of abstraction. How is system software different from application software?

Instruction set architecture acts as an interface between hardware and software. Provides commands to the processor to tell it what it needs to do. Instruction set architecture is the machine’s portion visible to the assembly language programmer. Also, it is the only way to interact with the hardware.

System Software Application Software

It is written in assembly language. It is written in High-Level Languages.

They are general-purpose software. They are made for specific purposes and functions.

They can run independently of application software. They need System Software to run.

It works as an interface between Application Software and System. It works as an interface between the Application Software and the User.

Users cannot interact with it. Users can interact with it and give them commands.

E.g., Operating Systems E.g., MS Word

Q5 “The clock rate of a computer may be arbitrarily increased to achieve faster execution by the CPU” – Do you agree with this statement? Explain.

No, we cannot increase the clock rate of a computer to a value to achieve high computations arbitrarily. Because improving the clock rate arbitrarily can increase the cost of the issues and lead to severe heating issues.

CPU execution time = (Number of instructions × CPI)(1/clock rate)

From the above equation, we can see that if the clock rate is inversely proportional, the execution time will decrease and vice-versa. If the pulse is high, given that the circuit is slow, it might be possible that the course is triggered by the next clock pulse, leading to the wrong result.

Q6. Is Moore’s Law still valid? Do you think that it will remain steadfast in the future? If not, what will be the possible reasons for its failure?

Moore’s Law is invalid nowadays because we cannot double the number of transistors every two years. According to a study, the growth rate of the number of transistors is less than 30% annually. Using standard silicon technology, computing power cannot maintain its rapid exponential growth.

In the future, the original Moore’s Law will not be valid. It will probably be replaced or modified according to the needs of quantum computation.

Reasons for the failure of Moore’s Law:

1. Currently, the transistors are soo small that we cannot reduce their size further. Now, Apple is making micro-chips of size 7nm, which is even minor in the thickness of hair.

2. The main reasons blocking Moore’s Law are Heating and Leakage.

3. Artificial Intelligence and Machine Learning have augmented Moore’s Law over time.

4. The upcoming era is Quantum Computing which has an entirely different structure and is based on nano-biotechnology. It will be the end of the silicon age.

Q7. Consider the following table.

Clock Cycles

ALU Instructions 50% 4

Load Instructions 30% 5

Store Instructions 10% 4

Branch Instructions 10% 2

Ans:

1) CPI is the average clock cycle required per instruction.

Therefore, CPI

= (50/100)×4 + (30/100)×5 + (10/100)×4 + (10/100)×2

= 4.1

2) CPU executing time = (No. of instructions)*CPI/frequency

Number of instructions = 3 × 106 {Given}

CPI = 4.1 {As calculated above}

Clock rate = 2 × 109 Hz {Given}

Therefore,

CPU execution time = (Number of instructions × CPI)(1/clock rate)

= (3 × 106 × 4.1)(1 / 2 × 109)

= 6.15 × 10-3s

3) Overall speedup (S0) = 1.5 {Given}

p = 0.5 {Given}

S = speedup {To find}

Further,

According to Amdahl’s Law, the Overall SpeedUp is given by-

S0 = 1(1 – p)+p/S {So is the overall speedup}

1.5 = 1(1 – 0.5)+(0.5/S)

Thus,

S = 3

Therefore, Speedup of ALU instruction is 3

In the first scheme, we will do the 1’s complement, but in the 2nd scheme, we will do the 2’s complement to increase the speed to the desired value.

Conclusion

In this article, we have discussed various essential questions about Computer Networks for Interview preparation.

There are also various topics in computer architecture, like the Arithmetic Logic Unit, which is responsible for all the arithmetic and logic operations on data processed by a computer. Also, the circuitry must be able to perform all the arithmetic and logic operations included in the instruction set.

Also, Control Units and Registers are some crucial topics I will discuss in subsequent blogs.

Currently, there is a massive demand for computer architecture engineers. So if you want to pursue this field, it is very high time. You get numerous resources online free of cost that will help you to master this skill.

4. Talk about Clock Rate, System Software, and Application Software.

It is all for today. I hope that you have enjoyed reading that article.

See you again 😊

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

50 Machine Learning Interview Questions And Answers (2023)

Here are Machine Learning interview questions and answers for fresher as well as experienced candidates to get their dream job.

1) What is Machine learning?

Machine learning is a branch of computer science which deals with system programming in order to automatically learn and improve with experience. For example: Robots are programed so that they can perform the task based on data they gather from sensors. It automatically learns programs from data.

2) Mention the difference between Data Mining and Machine learning?

Machine learning relates with the study, design and development of the algorithms that give computers the capability to learn without being explicitly programmed. While, data mining can be defined as the process in which the unstructured data tries to extract knowledge or unknown interesting patterns. During this process machine, learning algorithms are used.

3) What is ‘Overfitting’ in Machine learning?

In machine learning, when a statistical model describes random error or noise instead of underlying relationship ‘overfitting’ occurs. When a model is excessively complex, overfitting is normally observed, because of having too many parameters with respect to the number of training data types. The model exhibits poor performance which has been overfit.

4) Why overfitting happens?

The possibility of overfitting exists as the criteria used for training the model is not the same as the criteria used to judge the efficacy of a model.

5) How can you avoid overfitting?

By using a lot of data overfitting can be avoided, overfitting happens relatively as you have a small dataset, and you try to learn from it. But if you have a small database and you are forced to come with a model based on that. In such situation, you can use a technique known as cross validation. In this method the dataset splits into two section, testing and training datasets, the testing dataset will only test the model while, in training dataset, the datapoints will come up with the model.

In this technique, a model is usually given a dataset of a known data on which training (training data set) is run and a dataset of unknown data against which the model is tested. The idea of cross validation is to define a dataset to “test” the model in the training phase.

6) What is inductive machine learning?

The inductive machine learning involves the process of learning by examples, where a system, from a set of observed instances tries to induce a general rule.

7) What are the five popular algorithms of Machine Learning?

Decision Trees

Neural Networks (back propagation)

Probabilistic networks

Nearest Neighbor

Support vector machines

8) What are the different Algorithm techniques in Machine Learning?

The different types of techniques in Machine Learning are

Supervised Learning

Unsupervised Learning

Semi-supervised Learning

Reinforcement Learning

Transduction

Learning to Learn

9) What are the three stages to build the hypotheses or model in machine learning?

Model building

Model testing

Applying the model

10) What is the standard approach to supervised learning?

The standard approach to supervised learning is to split the set of example into the training set and the test.

11) What is ‘Training set’ and ‘Test set’?

In various areas of information science like machine learning, a set of data is used to discover the potentially predictive relationship known as ‘Training Set’. Training set is an examples given to the learner, while Test set is used to test the accuracy of the hypotheses generated by the learner, and it is the set of example held back from the learner. Training set are distinct from Test set.

12) List down various approaches for machine learning?

The different approaches in Machine Learning are

Concept Vs Classification Learning

Symbolic Vs Statistical Learning

Inductive Vs Analytical Learning

13) What is not Machine Learning?

Artificial Intelligence

Rule based inference

14) Explain what is the function of ‘Unsupervised Learning’?

Find clusters of the data

Find low-dimensional representations of the data

Find interesting directions in data

Interesting coordinates and correlations

Find novel observations/ database cleaning

15) Explain what is the function of ‘Supervised Learning’?

Classifications

Speech recognition

Regression

Predict time series

Annotate strings

16) What is algorithm independent machine learning?

Machine learning in where mathematical foundations is independent of any particular classifier or learning algorithm is referred as algorithm independent machine learning?

17) What is the difference between artificial learning and machine learning?

Designing and developing algorithms according to the behaviours based on empirical data are known as Machine Learning. While artificial intelligence in addition to machine learning, it also covers other aspects like knowledge representation, natural language processing, planning, robotics etc.

18) What is classifier in machine learning?

A classifier in a Machine Learning is a system that inputs a vector of discrete or continuous feature values and outputs a single discrete value, the class.

20) In what areas Pattern Recognition is used?

Pattern Recognition can be used in

Computer Vision

Speech Recognition

Data Mining

Statistics

Informal Retrieval

Bio-Informatics

21) What is Genetic Programming?

Genetic programming is one of the two techniques used in machine learning. The model is based on the testing and selecting the best choice among a set of results.

22) What is Inductive Logic Programming in Machine Learning?

Inductive Logic Programming (ILP) is a subfield of machine learning which uses logical programming representing background knowledge and examples.

23) What is Model Selection in Machine Learning?

The process of selecting models among different mathematical models, which are used to describe the same data set is known as Model Selection. Model selection is applied to the fields of statistics, machine learning and data mining.

24) What are the two methods used for the calibration in Supervised Learning?

The two methods used for predicting good probabilities in Supervised Learning are

Platt Calibration

Isotonic Regression

These methods are designed for binary classification, and it is not trivial.

25) Which method is frequently used to prevent overfitting?

When there is sufficient data ‘Isotonic Regression’ is used to prevent an overfitting issue.

26) What is the difference between heuristic for rule learning and heuristics for decision trees?

The difference is that the heuristics for decision trees evaluate the average quality of a number of disjointed sets while rule learners only evaluate the quality of the set of instances that is covered with the candidate rule.

27) What is Perceptron in Machine Learning?

In Machine Learning, Perceptron is a supervised learning algorithm for binary classifiers where a binary classifier is a deciding function of whether an input represents a vector or a number.

28) Explain the two components of Bayesian logic program?

Bayesian logic program consists of two components. The first component is a logical one ; it consists of a set of Bayesian Clauses, which captures the qualitative structure of the domain. The second component is a quantitative one, it encodes the quantitative information about the domain.

29) What are Bayesian Networks (BN)?

Bayesian Network is used to represent the graphical model for probability relationship among a set of variables.

30) Why instance based learning algorithm sometimes referred as Lazy learning algorithm?

Instance based learning algorithm is also referred as Lazy learning algorithm as they delay the induction or generalization process until classification is performed.

31) What are the two classification methods that SVM ( Support Vector Machine) can handle?

Combining binary classifiers

Modifying binary to incorporate multiclass learning

32) What is ensemble learning?

To solve a particular computational program, multiple models such as classifiers or experts are strategically generated and combined. This process is known as ensemble learning.

33) Why ensemble learning is used?

Ensemble learning is used to improve the classification, prediction, function approximation etc of a model.

34) When to use ensemble learning?

35) What are the two paradigms of ensemble methods?

The two paradigms of ensemble methods are

Sequential ensemble methods

Parallel ensemble methods

36) What is the general principle of an ensemble method and what is bagging and boosting in ensemble method?

The general principle of an ensemble method is to combine the predictions of several models built with a given learning algorithm in order to improve robustness over a single model. Bagging is a method in ensemble for improving unstable estimation or classification schemes. While boosting method are used sequentially to reduce the bias of the combined model. Boosting and Bagging both can reduce errors by reducing the variance term.

37) What is bias-variance decomposition of classification error in ensemble method?

The expected error of a learning algorithm can be decomposed into bias and variance. A bias term measures how closely the average classifier produced by the learning algorithm matches the target function. The variance term measures how much the learning algorithm’s prediction fluctuates for different training sets.

38) What is an Incremental Learning algorithm in ensemble?

Incremental learning method is the ability of an algorithm to learn from new data that may be available after classifier has already been generated from already available dataset.

39) What is PCA, KPCA and ICA used for?

PCA (Principal Components Analysis), KPCA ( Kernel based Principal Component Analysis) and ICA ( Independent Component Analysis) are important feature extraction techniques used for dimensionality reduction.

40) What is dimension reduction in Machine Learning?

In Machine Learning and statistics, dimension reduction is the process of reducing the number of random variables under considerations and can be divided into feature selection and feature extraction.

41) What are support vector machines?

Support vector machines are supervised learning algorithms used for classification and regression analysis.

42) What are the components of relational evaluation techniques?

The important components of relational evaluation techniques are

Data Acquisition

Ground Truth Acquisition

Cross Validation Technique

Query Type

Scoring Metric

Significance Test

43) What are the different methods for Sequential Supervised Learning?

The different methods to solve Sequential Supervised Learning problems are

Sliding-window methods

Recurrent sliding windows

Hidden Markow models

Maximum entropy Markow models

Conditional random fields

Graph transformer networks

44) What are the areas in robotics and information processing where sequential prediction problem arises?

The areas in robotics and information processing where sequential prediction problem arises are

Imitation Learning

Structured prediction

Model based reinforcement learning

45) What is batch statistical learning?

Statistical learning techniques allow learning a function or predictor from a set of observed data that can make predictions about unseen or future data. These techniques provide guarantees on the performance of the learned predictor on the future unseen data based on a statistical assumption on the data generating process.

46) What is PAC Learning?

PAC (Probably Approximately Correct) learning is a learning framework that has been introduced to analyze learning algorithms and their statistical efficiency.

47) What are the different categories you can categorized the sequence learning process?

Sequence prediction

Sequence generation

Sequence recognition

Sequential decision

48) What is sequence learning?

Sequence learning is a method of teaching and learning in a logical manner.

49) What are two techniques of Machine Learning?

The two techniques of Machine Learning are

Genetic Programming

Inductive Learning

50) Give a popular application of machine learning that you see on day to day basis?

The recommendation engine implemented by major ecommerce websites uses Machine Learning.

These interview questions will also help in your viva(orals)

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