Blue Yonder Interview Questions, Process, and Tips

Time and work,speed , logical reasoning

Q1. About projects done
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Q2. Why are you choosing this company
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Q1. Basic C program
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Q2. Diode working, DSA concept
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Q1. React native related questions
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Q2. Experince in developing mobile apps
Ans. 
I have 3 years of experience developing mobile apps for both iOS and Android platforms.
• Developed mobile apps using Swift for iOS and Java/Kotlin for Android

• Experience with mobile app design principles and user interface development

• Worked on integrating APIs and third-party libraries for enhanced functionality
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Q1. Generic interview
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Q1. Explain any one ML model to a non tech stakeholder?
Ans. 
Random Forest is a machine learning model that uses multiple decision trees to make predictions.
• Random Forest is an ensemble learning method that combines multiple decision trees to improve accuracy and reduce overfitting.

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

• The final prediction is made by averaging the predictions of all the individual trees in the forest.

• Rando...read more
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Q1. Explain back propagation?
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Back propagation is a technique used in neural networks to update the weights of the network by calculating the gradient of the loss function with respect to the weights.
• Back propagation involves calculating the error at the output layer and then propagating it back through the network to update the weights using gradient descent.

• It uses the chain rule of calculus to calculate the gradient of the loss function with res...read more
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Q2. Explain batch normalization?
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Batch normalization is a technique used in machine learning to improve the training of deep neural networks by normalizing the input of each layer.
• Batch normalization normalizes the input of each layer by adjusting and scaling the activations.

• It helps in reducing internal covariate shift, which leads to faster convergence during training.

• Batch normalization also acts as a regularizer, reducing the need for other regula...read more
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Q1. Explain transformer architechure?
Ans. 
Transformer architecture is a type of deep learning model that uses self-attention mechanism to process sequential data.
• Utilizes self-attention mechanism to weigh the importance of different input elements

• Consists of encoder and decoder layers for processing input and generating output

• Introduced in the paper 'Attention is All You Need' by Vaswani et al. in 2017
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Q2. How back propagation overtime works?
Ans. 
Back propagation over time is a method used in recurrent neural networks to update weights by propagating errors backwards through time.
• Back propagation through time (BPTT) is an extension of the backpropagation algorithm for recurrent neural networks.

• It involves unfolding the network over time and calculating gradients for each time step.

• The gradients are then used to update the weights of the network to minimize the ...read more
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