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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Blockchain Technology: Most Frequently Asked Interview Questions

Prashant
Prashant Last Updated : 08 Aug, 2022
6 min read

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

Introduction

Blockchain technology is a distributed ledger that maintains transaction information is encrypted, immutable, or non-modifiable records (called blocks).

A blockchain is a chain of information-containing blocks. Each block contains a log of the most recent transactions. When completed, it is added to the blockchain. When a block is completed, a new block is produced.

blockchain technology
(Source: blogs.iadb.org)

A blockchain may be used to safely transfer money, property, and contracts without needing a third-party middleman like a bank or government. Blockchain is a protocol for software, yet it could not function without the Internet (like SMTP used in email).

In this article, you will learn about blockchain technology, its key elements, and the frequently asked interview questions you must know. So, let’s begin.

Key Elements of a Blockchain

Distributed Ledger System

The distributed ledger and its immutable log of transactions are available to all network members. This shared ledger allows transactions to be logged simultaneously, reducing the effort duplication common to traditional business networks.

Immutable Record

After a transaction has been logged in the shared ledger, no participant may alter or manipulate it. If a transaction record has an error, a new transaction must be added to rectify it, and both transactions must then be available.

Smart contracts

A set of rules, considered a smart contract, is logged on the blockchain and automatically implemented to accelerate transactions. A smart contract can set criteria for transferring corporate bonds, stipulate payment terms for trip insurance, and much more.

Blockchain Technology Interview Questions

Below are some frequently asked interview questions you might want to know about.

1. What is cryptography? What function does it play in Blockchain?

Using cryptography and a hash function, Blockchain protects user identities and ensures the security of transactions. Using public and private keys, cryptography encrypts and decrypts data. A public key can be shared with all Bitcoin users on the blockchain network, while a private key (similar to a password) is kept hidden.

blockchain technology
(Source: 101blockchains.com)

Blockchain employs SHA-256, a safe algorithm that generates a unique hash for each input. The most important feature of this algorithm is that it creates a 64-char standardized alphanumeric output from every input. It is a one-way function from which an encrypted value may be derived from the input, but not the other way around.

2. What are the several Blockchain types?

There are three types of Blockchain: public, private, and consortium.

  • Public Blockchain ledgers are available to all internet users; every user can verify and contribute a transaction block to the Blockchain. Instances include Bitcoin and Ethereum.

  • Private Blockchain ledgers are available to internet users, but the only authorized person within an organization is allowed to analyze and add transactions. It is a permissioned blockchain, and although the information is publicly available, the information controllers are predefined inside the business, specifically, blockstack.

  • In Consortium Blockchain, only specified nodes manage the consensus procedure. However, all Blockchain consortium participants may view the ledgers. For example, Ripple.

3. What is the difference between Ethereum Blockchain and Bitcoin Blockchain?

A blockchain is a peer-to-peer distributed network. It allows peers to store data that is immutable and transparent. The approach taken by bitcoin and Ethereum differs from one another. Ethereum, as second-generation blockchain technology, outperforms bitcoin in nearly every manner.

The underlying differentiation is how they aim to address the industry challenge. Bitcoin is digital money, but Ethereum is concerned with smart contracts. Ethereum’s Proof-of-Stake (PoS) consensus method is more energy-efficient than bitcoin’s Proof-of-Work (PoW). Additionally, this makes Ethereum more scalable than bitcoin.

4. What are the key attributes of Blockchain?

There are several key attributes of blockchain:

  • As a data structure: Blockchain may store many kinds of data, like identification information, insurance, medical records, etc.

  • Detecting tampering: Once the data has been put on the blockchain, it is immutable. This provides the blockchain with the property of detecting tampering.

  • Protect Data: As the data owner is the source peer, the source is solely responsible for data protection. Additionally, the absence of third-party actors implies that it is safe and provides the highest level of data security.

  • Decentralized ledger technology: The most crucial aspect of a blockchain is its decentralized ledger technology. It has several applications for both public and private entities.

  • Improve user anonymity: Users are considerably concealed compared to other traditional networks.

  • Double Spending: Using consensus algorithms and distributed ledger technology, Blockchain resolves issues, including double-spending.

5. What kinds of data may be stored in a blockchain?

Since blockchain functions as a data structure, it may be used to store all types of data. Industries can use blockchain record types effectively since they can fully exploit its benefits. The following are the most frequent kinds of records and data that may be stored on a blockchain:

  • Identity management

  • Transaction handling

  • Documentation

  • Medical records

  • Management activities

  • Commercial transactions

6. What are Merkle trees? What is the importance of Merkle trees in Blockchains?

Merkle Tree, also called a hash tree, is a cryptographic data structure in which each leaf node is a hash of a data block, and each non-leaf node is a hash of its child nodes.

Instead of downloading every transaction and every block, a “light client” can merely download the chain of block headers when using the Merkle Trees in the blockchain.

(Source: geeksforgeeks.org)

Additionally, if a user needs to confirm the existence of a certain transaction in a block, he does not need to download the entire block. Downloading a set containing this transaction from a branch of this tree is sufficient. We examine the hashes currently ascending the branch (relevant to my transaction). If both hashes are valid, then we know that the transaction in question exists in this block.

7. Is Blockchain distinct from Bank Ledgers?

Banks and accounting systems use ledgers to record and time-stamp transactions. The blockchain, in contrast, is entirely decentralized and open source. Individuals do not need to rely on or trust the central bank to monitor transactions. The peer-to-peer blockchain technology can trace all transactions without the risk of them being lost or deleted.

Moreover, the blockchain is more versatile and configurable than central bank ledgers due to its open-source nature. If programmers require new capabilities on the blockchain, they can develop on top of current software through consensus. This is challenging for central banks due to their several restrictions and single points of failure.

8. List the stages involved in the implementation of the Blockchain project.

Requirement identification:

  • Identify the problem and aim

  • Determine the optimal consensus process

  • Determine the optimal platform

  • Costs associated with installation and deployment

Planning stage:

  • In this phase, a person assesses all requirements and selects an appropriate blockchain platform for implementation.

Development and enforcement of a project:

  • Developing the architecture

  • Designing a user interface

  • Developing APIs

Controlling and supervising the project:

  • Implementing Proof-of-Concept (POC)

  • identifying and resolving problems

9. What are Solidity function modifiers? Mention the most prevalent modifiers.

In Solidity, function modifiers immediately alter smart contract functions’ behavior. Simply said, it may provide new capabilities or impose constraints on the functionality of smart contracts. The most prevalent function modifiers in solidity include:

  • View, a non-modifiable function, cannot alter the condition of a smart contract. These are read-only methods.

  • Pure functions that neither read nor write a smart contract’s condition. They return the same output regardless of the input values.

10. What distinguishes a Blockchain distributed ledger from a traditional ledger?

A Blockchain distributed ledger is far more transparent than a traditional ledger.

Distributed blockchain ledgers are irreversible. Information cannot be updated on a distributed ledger, while it is reversible on a traditional ledger.

A distributed ledger provides more security. It employs cryptography, and each transaction is hashed and logged, while the security of traditional ledgers can be compromised.

A distributed ledger lacks a centralized authority. It is a distributed system in which the participants are responsible for maintaining the integrity of the network and validating the transactions. Traditional ledgers are founded on centralized control, which regulates all transactions.

In a distributed ledger, participant identities are unknown and concealed, while, in a traditional ledger, the identities of all participants must be known before transactions.

There is no single point of failure in a distributed ledger, as data is spread and shared among several nodes. If one node fails, the information is replicated on the other nodes. On the other hand, traditional ledgers have a single point of failure. If a single machine fails, the entire network is rendered inoperable.

It is impossible to modify or update data in a distributed ledger, but this is feasible in a traditional ledger.

In a distributed ledger, validation is performed by network members, while in a traditional ledger, validation is performed by a central authority.

Conclusion

This article outlines Blockchain Technology, which keeps transaction data from immutable or non-modifiable records, and covers basic, intermediate, and advanced interview questions. The following are some of the topics mentioned in this article:

  • What are Blockchain Technology and its key elements?

  • Types of Blockchain and the deployment phases for Blockchain Projects.

  • Key features of Blockchain and several kinds of records can be stored in Blockchain.

  • Merkel Trees, Bank Ledgers, Traditional ledgers, Ethereum Blockchain, Bitcoin Blockchain, and others.

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

Prashant
Prashant

Hello, my name is Prashant, and I'm currently pursuing my Bachelor of Technology (B.Tech) degree. I'm in my 3rd year of study, specializing in machine learning, and attending VIT University.

In addition to my academic pursuits, I enjoy traveling, blogging, and sports. I'm also a member of the sports club. I'm constantly looking for opportunities to learn and grow both inside and outside the classroom, and I'm excited about the possibilities that my B.Tech degree can offer me in terms of future career prospects.

Thank you for taking the time to get to know me, and I look forward to engaging with you further!

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