LLM & Gen AI Interview Questions (with Explanation)

Our meticulously designed practice tests keeps pace with the AI industry's latest advancements, covers both depth and breath, while concentrating on the important topics including Model Architectures of LLM Models like GPT, LLama,  LLM Pretraining, LLM fine-tuning techniques like LORA, BERT Model, DistilBERT,  CLIP,   Hugging Face library, Transformers Architecture, Attention Mechanism,  Model Compression techniques such as Knowledge Distillation and Quantization, Diffusion Models, Multimodal models, Prompt Engineering, Retrieval Augmented Generation (RAG) Systems, Embedding Models, Vector Databases and more. Additionally, the course features real questions that have been asked by leading tech companies.

Sample Questions:

1. What is the role of an attention mask in Transformer models?

2. How does RoBERTa handle token masking differently than BERT during training?

3. How are the dimensions of the Q, K, and V matrices determined in BERT?

4. How can temperature scaling be used in the knowledge distillation process?

5. For a BERT model with an embedding size of 1024 and 24 layers, how many parameters are in the embedding layer if the vocabulary size is 50,000?

6. How do LangChain agents interact with external databases?

7. What is the transformers.DataCollatorForLanguageModeling used for?

8. How does the discriminator's architecture typically compare to the generator's in a GAN?

9. What is the purpose of the conditional_prompt method in LangChain?

10. How can RAG systems handle ambiguous queries effectively?

Prepare comprehensively for Generative AI and Large Language Models (LLM) Engineer interviews with our dynamic Udemy course, "LLM & Gen AI Engineer Interview Questions (with Explanation)"

You'll also delve into questions that test conceptual and practical implementation of LLM & Gen AI based solutions using PyTorch and TensorFlow Frameworks, ensuring you're well-prepared to tackle any technical challenge in your interview.

This course evolves every month with 100+ NEW questions added every month to reflect the ever-changing landscape of LLMs and Generative AI Models.

Topics Covered in the Course:-

1. Model Architectures of Transformer & LLM Models like GPT, LLama, BERT

2. Hugging Face Transformers Library

3. Model Compression Techniques - Quantization & Knowledge Distillation

4. LLM Model - Pretraining, Fine-tuning & Alignment Techniques - PEFT, LORA, RLHF, DPO, PPO

5. Embedding Models

6. Diffusion Models

7. Vision Language Models

8. Multimodal Models

9. Retrieval Augmented Generation Systems (RAGs) - LangChain

10. Vector Databases

11. LLM Model Deployment

12. LLM Model Evaluation Metrices

13. Distributed LLM Model Training