Blue Yonder Interview Questions and Answers

Time and work,speed , logical reasoning

Q1. About projects done
Add your answer
Q2. Why are you choosing this company
Ans. 
I am drawn to this company for its innovative approach, commitment to quality, and strong values that align with my career aspirations.
• The company's focus on cutting-edge technology aligns with my passion for innovation, as seen in their recent product launch.

• I admire the company's commitment to sustainability, which resonates with my personal values and desire to make a positive impact.

• The collaborative culture here f...read more
Answered by AI
Add your answer

Q1. Basic C program
Ans. 
A basic C program typically includes headers, main function, and can perform simple tasks like printing text or calculations.
• Include necessary headers: `#include <stdio.h>` for input/output functions.

• Define the main function: `int main() { ... }` serves as the entry point.

• Use `printf` to display output: `printf("Hello, World!\n");` prints text.

• Return an integer value: `return 0;` indicates successful execution.
Answered by AI
Add your answer
Q2. Diode working, DSA concept
Add your answer

Q1. React native related questions
Add your answer
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
Answered by AI
Add your answer

Q1. Generic interview
Add your answer

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
Answered by AI
Add your answer

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
Answered by AI
Add your answer
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
Answered by AI
Add your answer

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
Answered by AI
Add your answer
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
Answered by AI
Add your answer