DRPU Software Interview Questions and Answers

Mathematical and logical questions including distance -time, work -time, reasoning, blood relation etc

Q1. Database queries including joins.
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DSA moderate question like string reverse, string rotation, frequency of letter in a string.

It has general aptitude questions with moderate difficulty

It was mixed coding plus SQL question after the exam I had explain my solution.

Simple questions of quant and coding output

3 coding question and one pattern question

Q1. Discussions on questions of prev round
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Q2. One SQL query need to implement
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MCQ - 20 (aptitude), 20(c+ and sql)

Pattern, array and sql questions

Giva a Full stack project. based on MERN AND MEAN stack

Q1. Dsa , were easy to medium
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Q2. Some backend and azure related questions
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Q1. C and Cpp all concepts
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Q1. DSA - Linked List, Array and Strings
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Q1. Why should we hire you
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Q1. What is hyperparameter tuning?
Ans. 
Hyperparameter tuning is the process of selecting the best set of hyperparameters for a machine learning model.
• Hyperparameters are parameters that are set before the learning process begins.

• Hyperparameter tuning involves adjusting hyperparameters to optimize the model's performance.

• Common techniques for hyperparameter tuning include grid search, random search, and Bayesian optimization.
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Q2. How do we train neural networks?
Ans. 
Neural networks are trained using algorithms that adjust the weights and biases of the network based on the input data and desired output.
• Neural networks are trained using a process called backpropagation, where the error between the predicted output and the actual output is used to adjust the weights and biases of the network.

• Training data is fed into the neural network, and the network's output is compared to the des...read more
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Q3. What is overfitting?
Ans. 
Overfitting occurs when a machine learning model learns the training data too well, including noise and outliers, leading to poor generalization on new data.
• Overfitting happens when a model is too complex and captures noise in the training data.

• It can be identified when a model performs well on training data but poorly on unseen data.

• Techniques to prevent overfitting include cross-validation, regularization, and early ...read more
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