Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

Reading list

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

12 FAQs on AWS Asked in Interviews

Pinak
Pinak Last Updated : 13 Oct, 2024
5 min read

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

Introduction

The way big business tycoons run has changed a lot since the past. The concept of “Cloud Computing” has played a major role in this. This implementation of cloud computing technology has led to the need for Cloud Computing Experts. The software team must be familiar with the Amazon Web Services (AWS) cloud-storage facility to stay abreast with leading technologies, one of the compositions of the “Hot Topics List” in the market for their high utility value. So to kick-start your career as a developer in the field of cloud computing, and excel in the interviews with flying colors, below are some curated questions that are often asked in the interviews.

AWS Interview Questions

1) What do you mean by cloud computing?

Cloud computing performs operations on IT resources such as systems, stages, or programs as their organizations are utilized over the Net with a pay-per-use preface. Cloud advantage suppliers are companies with open cloud or information centres that offer operations like compute, capacity, database, operations, development, exhorting, and analytics.

Cloud Computing is broad of three types :

Infrastructure as a Service(Iaas), Platform as a Service (PaaS), Software as a Service(SaaS)

2) What are the major benefits of Cloud Computing?

The major benefits include :

1) Cloud Computing enhances security. It uses many industry-level protocols like HIPAA.

2) As cloud computing is relatively cheaper, it is often a good option for startups.

3) Cloud Computing companies promise their clients 100% data backup to ensure the crucial data is never lost.

3) Name the major components of AWS.

The major components include :
  1. Elastic Cloud Compute (EC2): EC2 is a service provided by Amazon to enable developers’ cloud computing facilities.
  2. Simple Storage Service (S3): It is a storage platform used to store and fetch any information over the internet.
  3. Identity and Access Management(IAM): provides the service of super-secure functioning on the services of AWS.
  4. Route 53: It is a service provided by AWS to route end-user applications in an economical and routine fashion.
  5. Elastic Block Store (EBS) provides block-storage volumes to store persistent data.
  6. CloudWatch: It is used to get an insight and monitor the applications and their infrastructures.

4) Name some major differences between Simple Storage Service (S3) and Electronic Block Storage(EBS).

The major differences between the two are as:

AWS

Image source: https://jayendrapatil.com/aws-s3-vs-ebs-vs-efs/

5) Brief about the relationship between an “Instance” and “AMI”.

We can create multiple instances from a single AMI. We can easily communicate with the instance, treating it like any node on the internet.An AMI includes the “Permission to Launch” to choose from which accounts in AWS will be able to launch the instances. It will also contain mapping to find the amount of volume to link with an instance at that time of launch.

6)What do you understand by “key-pair” in AWS?

We need to securely store the login details when we use any virtual machine. That is how “Key – value” pairs help us. It helps us to connect to the instances we launched.

7) What is DynamoDB?

It is a database of the NoSQL category. It is pervasive and can easily be connected with AWS. It is extremely scalable and fast. It takes care of all the necessary tasks, like clustering, replication, etc.

8) What is Elastic Beanstalk?

In a nutshell, Elastic Beanstalk is the service of execution of functional operations, involved in designing and doing an end-to-end service provided by AWS. It is used in various AWS apps, like EC2 and Load Balancers; it is one of the easiest and simplest ways of publishing your app on AWS.

9) Explain RedShift and SQS in brief.

RedShift is a huge data warehouse (amounts in petabytes). It is simple to use, affordable and can be manually customized per your current business needs.SQS on the other hand, is an acronym for Simple Query Service. It is a text-message enqueuing service that acts as the medium between two separate controllers.

10) What do you understand by “Hybrid Cloud Architecture” and “Configuration Management”?

“Hybrid Cloud Architecture” basically refers to splitting the workload into two halves: the public load and the 2nd one local storage. It is a conglomerate of public and private cloud services between two platforms.

“Configuration Management”, on the other hand, manages the configuration files and settings that are provided by their code-base. It is an extensively recursive and iterative process that REST APIs majorly achieve.

11) What are the lifecycle hooks in AWS autoscaling?

Lifecycle Hooks allow us to perform custom actions by stopping the instances where the autoscaling cluster is terminated and launched.
Python Code:

# importing required libraries
import pandas as pd
from xgboost import XGBClassifier
from sklearn.metrics import accuracy_score

# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')

# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)

# Now, we need to predict the missing target variable in the test data
# target variable - Survived

# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Survived'],axis=1)
train_y = train_data['Survived']

# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Survived'],axis=1)
test_y = test_data['Survived']

'''
Create the object of the XGBoost model
You can also add other parameters and test your code here
Some parameters are : max_depth and n_estimators
Documentation of xgboost:

https://xgboost.readthedocs.io/en/latest/
'''
model = XGBClassifier()

# fit the model with the training data
model.fit(train_x,train_y)


# predict the target on the train dataset
predict_train = model.predict(train_x)
print('\nTarget on train data',predict_train)

# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print('\naccuracy_score on train dataset : ', accuracy_train)

# predict the target on the test dataset
predict_test = model.predict(test_x)
print('\nTarget on test data',predict_test)

# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('\naccuracy_score on test dataset : ', accuracy_test)

12) What is “logging” in CloudFront? What is CloudWatch?

 CloudFront is used to enable or disable the logging. The “Logs” contains all the basic information like date, time, etc. When enabled, the logs are stored in the S3 Buckets, which in the future can be analyzed for other purposes.

CloudWatch is a storehouse for metrics. It is used to monitor the applications and services. We can take the help of the past events data to automate the tasks and reduce the time consumed, here measured in units of MTTR (Mean Time To Resolution).

Conclusion

Cloud Computing is going to be a booming field for IT and other businesses as well. AWS is one of the most commonly utilized cloud storage at the show. Different companies have embraced AWS to store information and develop a framework for their business. Whether you’re a Fresher or someone who has prior experience, preparing yourself with these AWS Meet Questions and Answers will assist you ensured victory in your AWS journey that lies ahead.

So, ending with some info-bytes summed up in a Nutshell :

1. AWS has three major products that are used – EC2, LightSail, and Lambda.

2. The main features of AWS that make it so useful – Scalability, Security, and Customizability.

3. A SnowBall is an app that allows users to send and receive terra-bytes of data within the AWS environment

I hope that these questions will help you in your AWS interviews shortly. The questions are collected from various sources, including some interview experiences for some present AWS developers.

That is all from my side for now. If you have any doubts, feel free to ping me on Linked-in.

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

Pinak
Pinak

Hi, I'm Pinak Datta, currently pursuing my B.Tech in Computer Science and Engineering from Kalinga Institute of Industrial Technology. I'm in my third year of study and I've always had a keen interest in technical writing and software development. I love to develop programs and scripts using Python and have worked on several projects in this language.

Apart from my academic pursuits, I've also participated in various hackathons and coding competitions. These experiences have allowed me to showcase my creativity and problem-solving abilities in the field of computer science.

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details