Blue Yonder Interview Questions, Process, and Tips

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. Explain microservices architecture.
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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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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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Moderate level coding que leetcode type...

Q1. Asked basic concept of oops
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Q2. Few question from git and github....
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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
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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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