Info Edge Interview Questions, Process, and Tips

Q1. Tell me about yourself?
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Q1. Why you want to work as a lead quality management?
Ans. 
I am passionate about improving processes and ensuring high quality standards are met.
• I have a strong background in quality management and enjoy leading teams to success.

• I am motivated by the opportunity to make a positive impact on the organization's overall performance.

• I thrive in roles that require attention to detail and problem-solving skills.

• I have experience implementing quality improvement initiatives, such as
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Its normal gd with topics like offilne vs online eduction, indoor vs outdoor game .

Q1. Introduce yourself
Ans. 
Experienced professional with a background in project management and team leadership.
• Over 8 years of experience in project management

• Led cross-functional teams to successful project completion

• Strong communication and problem-solving skills

• MBA in Business Administration from XYZ University
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Q2. About your internship
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Q1. Two stack based question
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Q2. Find the non repeting no from an array
Ans. 
Find the non-repeating number in an array of strings.
• Iterate through the array and count the occurrences of each number using a hashmap.

• Return the number that has a count of 1.
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Mcq based test on data science concepts

Q1. Explain precision,recall etc
Ans. 
Precision and recall are metrics used to evaluate the performance of classification models.
• Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.

• Recall is the ratio of correctly predicted positive observations to the all observations in actual class.

• F1 score is the weighted average of precision and recall, where the best value is 1 and the worst is 0.

• Precision ...read more
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Q2. What is dropout in neural networks
Ans. 
Dropout is a regularization technique used in neural networks to prevent overfitting by randomly setting some neuron outputs to zero during training.
• Dropout is a regularization technique used in neural networks to prevent overfitting.

• During training, a fraction of neurons are randomly selected and their outputs are set to zero.

• This helps prevent complex co-adaptations in neurons and improves generalization.

• Dropout is t...read more
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