Deep Learning & Generative AI

Generative AI & Large Language Models (LLMs) Interview Questions

Interview questions on Generative AI & Large Language Models (LLMs) Interview Questions.

15 questions

All subtopics LLM Architectures & Foundations Tokenization & Embeddings Attention, KV Cache & Efficient Inference LLM Training & Pre-training Decoding Strategies & Prompt Engineering Fine-Tuning & Model Adaptation RAG & Vector Databases Alignment & Preference Optimization LLM Evaluation & Benchmarking Agents, Tool Use & Memory Multimodal Generative AI LLM Safety & Security LLMOps & Production

Attention, KV Cache & Efficient Inference

Q1. How does the attention mechanism function in transformer models?

Attention, KV Cache & Efficient Inference

Q2. What are the main steps involved in the attention mechanism?

Attention, KV Cache & Efficient Inference

Q3. What is the KV cache in transformer models?

Attention, KV Cache & Efficient Inference

Q4. What are the advantages and disadvantages of using a Key-Value (KV) cache in Transformer models?

Attention, KV Cache & Efficient Inference

Q5. What is model distillation, and how does it benefit LLMs?

Attention, KV Cache & Efficient Inference

Q6. How do transformers improve on traditional Seq2Seq models?

Attention, KV Cache & Efficient Inference

Q7. What is multi-head attention, and how does it enhance LLMs?

Attention, KV Cache & Efficient Inference

Q8. What is local (sparse) attention and how does it work?

Attention, KV Cache & Efficient Inference

Q9. What is Flash Attention?

Attention, KV Cache & Efficient Inference

Q10. How is the softmax function applied in attention mechanisms?

Attention, KV Cache & Efficient Inference

Q11. How does the dot product contribute to self-attention?

Attention, KV Cache & Efficient Inference

Q12. How are attention scores calculated in transformers?

Attention, KV Cache & Efficient Inference

Q13. How does Mixture of Experts(MoE) enhance LLM scalability?

Attention, KV Cache & Efficient Inference

Q14. How do transformers address the vanishing gradient problem?

Attention, KV Cache & Efficient Inference

Generative AI & Large Language Models (LLMs) Interview Questions

Q1. How does the attention mechanism function in transformer models?

Q2. What are the main steps involved in the attention mechanism?

Q3. What is the KV cache in transformer models?

Q4. What are the advantages and disadvantages of using a Key-Value (KV) cache in Transformer models?

Q5. What is model distillation, and how does it benefit LLMs?

Q6. How do transformers improve on traditional Seq2Seq models?

Q7. What is multi-head attention, and how does it enhance LLMs?

Q8. What is local (sparse) attention and how does it work?

Q9. What is Flash Attention?

Q10. How is the softmax function applied in attention mechanisms?

Q11. How does the dot product contribute to self-attention?

Q12. How are attention scores calculated in transformers?

Q13. How does Mixture of Experts(MoE) enhance LLM scalability?

Q14. How do transformers address the vanishing gradient problem?

Q15. How do encoders and decoders differ in transformers?