Blue Yonder Interview Questions, Process, and Tips

Q1. Explain back propagation?
Ans. 
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?
Ans. 
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?
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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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Q1. Java, streams,coding
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Q1. Java Basics like jdk, jre, core java questions
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Q2. Merge sort
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Q1. 2 simple dsa questions and asked about javascript and react questions
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Q1. Asked react advance concepts and one machiene coding round
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Q1. Explain microservices architecture.
Ans. 
Microservices architecture is an architectural style that structures an application as a collection of loosely coupled services.
• Each service is self-contained and can be developed, deployed, and scaled independently.

• Services communicate with each other over lightweight protocols like HTTP or messaging queues.

• Microservices allow for better scalability, flexibility, and resilience compared to monolithic architectures.

• Exa...read more
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Q1. Basics of oops concepts,testing concepts,SQL
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Q2. Make your introduction strong
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I am a highly skilled Technical Consultant with a proven track record of successfully implementing complex solutions for clients.
• Highlight key technical skills and experience

• Mention specific projects or achievements

• Emphasize problem-solving abilities and client satisfaction
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Q1. Situation on how to manage a team crucial times
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Q2. How to go along with employees having diffrence of opinion
Ans. 
Listen to their perspectives, encourage open communication, find common ground, and work towards a compromise.
• Encourage open communication to understand each employee's perspective

• Listen actively and show empathy towards their opinions

• Find common ground and areas of agreement to build upon

• Work towards a compromise that addresses the concerns of all parties involved
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Calculate the similar strings in a list

Q1. Explain Batch/Layer Normalization
Ans. 
Batch normalization is a technique used 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, making training faster and more stable.

• Batch normalization is applied to mini-batches of data during training.

• Layer normalization is similar to batch no...read more
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Q2. Explain symmetry trees in Catboosy
Ans. 
Symmetry trees in Catboost are used to improve the model's performance by exploiting the symmetry of categorical features.
• Symmetry trees are a type of decision tree used in Catboost to handle categorical features.

• They exploit the symmetry of categorical features to improve the model's performance.

• Symmetry trees help in reducing overfitting and improving generalization.

• They are particularly useful when dealing with high...read more
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