Cybage Data Scientist Interview Questions and Answers

Q1. Basic Data Science concepts
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Q1. Basic HR round and discussion about the salary
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Q1. Stated it was client preparation round but senior managed showed and just asked about the briefs.
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Q1. Coding question of finding index of 2 nos. having total equal to target in a list, without using nested for loop? l= [2,15,5,7] t= 9 output》》[0,3]
Ans. 
Finding index of 2 numbers having total equal to target in a list without nested for loop.
• Use dictionary to store the difference between target and each element of list.

• Iterate through list and check if element is in dictionary.

• Return the indices of the two elements that add up to target.
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Q2. What is random forest, knn?
Ans. 
Random forest and KNN are machine learning algorithms used for classification and regression tasks.
• Random forest is an ensemble learning method that constructs multiple decision trees and combines their outputs to make a final prediction.

• KNN (k-nearest neighbors) is a non-parametric algorithm that classifies new data points based on the majority class of their k-nearest neighbors in the training set.

• Random forest is us...read more
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Q1. Ll coding on python dictionary
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Q2. Find unique keys in 2 dictionaries
Ans. 
To find unique keys in 2 dictionaries.
• Create a set of keys for each dictionary

• Use set operations to find the unique keys

• Return the unique keys
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Q3. Aws ec2 model deployment procedure
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AWS EC2 model deployment involves creating an instance, installing necessary software, and deploying the model.
• Create an EC2 instance with the desired specifications

• Install necessary software and dependencies on the instance

• Upload the model and any required data to the instance

• Deploy the model using a web server or API

• Monitor the instance and model performance for optimization
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Q4. Overloading concept of oop
Ans. 
Overloading is the ability to define multiple methods with the same name but different parameters.
• Overloading allows for more flexibility in method naming and improves code readability.

• Examples include defining multiple constructors for a class with different parameter lists or defining a method that can accept different data types as input.

• Overloading is resolved at compile-time based on the number and types of argume...read more
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Q1. When do we use PCA?
Ans. 
PCA is used to reduce the dimensionality of data by finding the most important features.
• PCA is used when dealing with high-dimensional data to reduce the number of features and avoid multicollinearity.

• It helps in visualizing data in lower dimensions while retaining as much variance as possible.

• PCA is commonly used in image processing, genetics, finance, and other fields where dimensionality reduction is needed.
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Q2. PCA is a dimensionality reduction technique and is used to reduce the less useful features from the dataset
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Q1. LTTS data science interview
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Q1. More ML and DL based questions. What is random forest. What is neural network. Early Stopping, Weights and Bias, Bagging and Boosting.
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Q2. Linear Regression and Logistics Regression and difference between both.
Ans. 
Linear Regression predicts continuous values while Logistic Regression predicts binary outcomes.
• Linear Regression is used for predicting continuous values while Logistic Regression is used for predicting binary outcomes.

• Linear Regression uses a linear approach to model the relationship between dependent and independent variables while Logistic Regression uses a logistic function to model the probability of a binary out...read more
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Q1. Deep dive into your DS projects and implementation. Some questions about MLOPS and cloud exposure. Basic ML questions
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Q2. Gradient descent Linear regression Time series questions Lightgbm and xgboost
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Q1. Time series question on ARIMA , ML approach to time series
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SQL, select statment and DDL commands

Q1. Python lists and conditional statements and loos
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Q1. General machine learning and data science related questions
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Q1. Focussing on project and related questions
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Q1. About the career prospects and other interests
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