Tatvasoft Team Lead Interview Questions and Answers

Q1. Teall me about urself
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I was asked 2 programming questions.

Q1. Scenario based question. What if someone creates new user with 1st name and last name but backend developer forgot to write code to store last name. How will data be corrected.
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Q1. General .net core, oops, sql, caching, authentication, security, angular.
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Q1. Skill and technology worked
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I have worked with various technologies and developed skills in project management, team leadership, and software development.
• Proficient in project management tools such as JIRA and Trello

• Experience in leading teams of up to 10 members

• Skilled in programming languages such as Java, Python, and JavaScript

• Familiarity with front-end frameworks such as React and Angular

• Knowledge of database technologies such as MySQL and Mo
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Q2. Give only honest reply
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Q1. Basic and semi advance
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Q2. Clear the basic and try hands on experince
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Q1. Advance level of power bi and SQL
Ans. 
I have an advanced level of Power BI and SQL skills.
• Expertise in creating complex data models and DAX formulas in Power BI

• Proficient in writing complex SQL queries and stored procedures

• Experience in integrating Power BI with SQL Server and other data sources

• Ability to optimize queries and improve performance

• Familiarity with data visualization best practices and design principles
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Q2. Can get idea if worked aroud 7 years
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Q1. Why are you looking for a change?
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Q2. Where do you see yourself in 5 years?
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Q1. Technical and real life scenario
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Q1. Technical, use case, real life scenario
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Q1. General knowledge
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Q1. HR round for qualification and negotiation
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Q1. Explain random forest
Ans. 
Random forest is an ensemble learning method that builds multiple decision trees and merges them to improve accuracy and prevent overfitting.
• Random forest is a type of ensemble learning method.

• It builds multiple decision trees during training.

• Each tree is built using a subset of the training data and a random subset of features.

• The final prediction is made by averaging the predictions of all the individual trees.

• Random...read more
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Q2. What is boosting
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Boosting is a machine learning ensemble technique where multiple weak learners are combined to create a strong learner.
• Boosting is an iterative process where each weak learner is trained based on the errors of the previous learners.

• Examples of boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.

• Boosting is used to improve the accuracy of models and reduce bias and variance.
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Q1. .NET MVC, .NET Core, .NET
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Q2. MS SQL, MongoDB, NoSQL
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