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Infosys Interview Questions and Answers

Updated 5 Feb 2024

Q1. What's input data

Ans.

Input data is the information that is entered into a system for processing.

  • Input data can be in the form of text, numbers, images, or any other type of information.

  • Examples of input data include customer names, addresses, product prices, and sales figures.

  • Input data is essential for performing data entry tasks accurately and efficiently.

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Q2. Short key of Open Ms Word

Ans.

Ctrl + O

  • Press Ctrl key and O key together

  • Shortcut to open a new document in Microsoft Word

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Q3. What's output data

Ans.

Output data is the information generated by a system or process as a result of input data being processed.

  • Output data is the final result of processing input data.

  • It can be in the form of reports, charts, graphs, or any other format depending on the system.

  • Examples include financial statements generated from accounting data, sales reports from CRM systems, or statistical analysis from research data.

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Q4. Which model is used in Credit card detection?

Ans.

Credit card detection uses machine learning models.

  • Machine learning models like logistic regression, decision trees, and neural networks are used for credit card detection.

  • These models analyze patterns in credit card transactions to detect fraudulent activity.

  • The models are trained on large datasets of both legitimate and fraudulent transactions.

  • The models can be updated and improved over time as new data becomes available.

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Q5. Full Name of RAM

Ans.

Random Access Memory

  • RAM stands for Random Access Memory

  • It is a type of computer memory that can be accessed randomly

  • Examples include DDR4, DDR3, and LPDDR4

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Q6. What is confusion matrix?

Ans.

A confusion matrix is a table used to evaluate the performance of a classification model.

  • It shows the number of true positives, true negatives, false positives, and false negatives.

  • It helps in calculating various evaluation metrics like accuracy, precision, recall, and F1 score.

  • It is useful in identifying the strengths and weaknesses of a model and improving its performance.

  • Example: A confusion matrix for a binary classification problem can look like this: Actual Positive Act...read more

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