Infosys Interview Questions, Process, and Tips

Q1. What are golden rules of accounting.
Ans. 
Golden rules of accounting are basic principles that guide the process of recording financial transactions.
• The three golden rules of accounting are: Debit what comes in, Credit what goes out; Debit the receiver, Credit the giver; Debit expenses and losses, Credit income and gains.

• These rules help ensure that financial transactions are accurately recorded and classified in the accounting system.

• For example, when a compa...read more
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Q2. Self inteoduction
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Q1. How soon I can join
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Q2. Why you want to join
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100 mins test covered almost all the areas can get the syllabus to practice from yputube

Q1. Basics oops DSA Java SQL
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Q1. General Question about my resume
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Q2. Technical questions about the skillset
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Q1. Offer confirmation
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Q2. Salary discussion
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Q1. What is evaluation Matrix for classification
Ans. 
Evaluation metrics for classification are used to assess the performance of a classification model.
• Common evaluation metrics include accuracy, precision, recall, F1 score, and ROC-AUC.

• Accuracy measures the proportion of correctly classified instances out of the total instances.

• Precision measures the proportion of true positive predictions out of all positive predictions.

• Recall measures the proportion of true positive p...read more
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Q2. What L1 and L2 regression
Ans. 
L1 and L2 regression are regularization techniques used in machine learning to prevent overfitting.
• L1 regression adds a penalty equivalent to the absolute value of the magnitude of coefficients.

• L2 regression adds a penalty equivalent to the square of the magnitude of coefficients.

• L1 regularization can lead to sparse models, while L2 regularization tends to shrink coefficients towards zero.

• L1 regularization is also know...read more
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Q3. Explain random forest algorithm
Ans. 
Random forest is an ensemble learning algorithm that builds multiple decision trees and combines their predictions.
• Random forest creates multiple decision trees using bootstrapping and feature randomization.

• Each tree in the random forest is trained on a subset of the data and features.

• The final prediction is made by averaging the predictions of all the trees (regression) or taking a majority vote (classification).
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Q1. Tell me about self
Ans. 
I am a dedicated and passionate Machine Learning Engineer with a strong background in computer science and data analysis.
• Experienced in developing machine learning models for various applications

• Proficient in programming languages such as Python, R, and Java

• Skilled in data preprocessing, feature engineering, and model evaluation

• Strong understanding of algorithms and statistical concepts

• Excellent problem-solving and ana
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Q2. Questions about salary discuss
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Q1. Constructor , DI, Opps Question
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Q2. Design Pattern , MVC and Net core Question
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