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882 algorithm interview questions shared by candidates

During both interviews, I was asked quite ordinary questions, with a strong focus on motivation. I also was repeatedly asked to ask questions. In the second interview, I additionally had to make a short presentation and to answer questions designed to test my reasoning process.
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Optimization Expert/Algorithm Expert

Interviewed at Dassault Systèmes

3.9★
May 28, 2015

During both interviews, I was asked quite ordinary questions, with a strong focus on motivation. I also was repeatedly asked to ask questions. In the second interview, I additionally had to make a short presentation and to answer questions designed to test my reasoning process.

Technical Assessment (20 minutes) LeetCode Question: Longest Palindromic Substring Coding live where the interviewer can see your code on a shared editor. Discussion and Questions (40 minutes) Self-Introduction Project Discussion: Explain a project related to LLM LLM Creation: How is a Large Language Model created? Model Architectures: Explain the different models and their architectures (e.g., GPT, LLAMA, Falcon, BLOOM) Positional Embeddings: What is the purpose of positional embeddings? Normalization Techniques: Difference between BatchNorm and LayerNorm Retrieval-Augmented Generation (RAG): - What is RAG? Explain its purpose. - Even if you use RAG, there are hallucinations occurring. Why is this so and what can you do to mitigate? - What other current academic advancements in RAG? Explain about some frameworks that are trending. Scenario Question: If we have large-scale data (billions of records) from the web, how can I efficiently select math-related data? Discuss using distributed computing frameworks if possible.
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Algorithm Engineer, Large Language Model

Interviewed at Shopee

3.7★
Nov 5, 2024

Technical Assessment (20 minutes) LeetCode Question: Longest Palindromic Substring Coding live where the interviewer can see your code on a shared editor. Discussion and Questions (40 minutes) Self-Introduction Project Discussion: Explain a project related to LLM LLM Creation: How is a Large Language Model created? Model Architectures: Explain the different models and their architectures (e.g., GPT, LLAMA, Falcon, BLOOM) Positional Embeddings: What is the purpose of positional embeddings? Normalization Techniques: Difference between BatchNorm and LayerNorm Retrieval-Augmented Generation (RAG): - What is RAG? Explain its purpose. - Even if you use RAG, there are hallucinations occurring. Why is this so and what can you do to mitigate? - What other current academic advancements in RAG? Explain about some frameworks that are trending. Scenario Question: If we have large-scale data (billions of records) from the web, how can I efficiently select math-related data? Discuss using distributed computing frameworks if possible.

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