Prime Technologies Interview Questions and Answers

It consists of basic aptitude questions and some coding output questions followed by basic SQL questions. Overall the difficulty level was medium.

Q1. They asked me to write code for 4 problems. All questions consists array medium level and then there are difficult SQL questions to solve. Unfortunately I couldn't passed this round. But it was a great lea...read more
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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

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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