LLM Gen AI Interview Questions (with Explanation)

LLM & Gen AI Interview Questions (with Explanation), available at $44.99, has an average rating of 4.83, 6 quizzes, based on 3 reviews, and has 28 subscribers.

You will learn about Questions commonly asked in AI Engineer role interviews Questions commonly asked in Generative AI/LLM Engineer role interviews Test your knowledge & Technical Interview Preparation in LLM & Transformer Models, LLM Pretraining & fine-tuning techniques like LORA, QLORA, RLHF Test your knowledge of Text Models & their Model Architectures like BERT, DistilBERT, Roberta, T5 Models & More Conceptual & Practical Implementation based questions on RAGs, LangChain, Vector Databases & AI Model Compression, Deployment, Distributed Training Techniques This course is ideal for individuals who are Data Scientists or AI Engineers or Machine Learning Engineers or Generative AI Engineers or Computer Science Graduate Students or Python Developers It is particularly useful for Data Scientists or AI Engineers or Machine Learning Engineers or Generative AI Engineers or Computer Science Graduate Students or Python Developers.

Enroll now: LLM & Gen AI Interview Questions (with Explanation)

Summary

Title: LLM & Gen AI Interview Questions (with Explanation)

Price: $44.99

Average Rating: 4.83

Number of Quizzes: 6

Number of Published Quizzes: 6

Number of Curriculum Items: 6

Number of Published Curriculum Objects: 6

Number of Practice Tests: 6

Number of Published Practice Tests: 6

Original Price: ?799

Quality Status: approved

Status: Live

What You Will Learn

Questions commonly asked in AI Engineer role interviews

Questions commonly asked in Generative AI/LLM Engineer role interviews

Test your knowledge & Technical Interview Preparation in LLM & Transformer Models, LLM Pretraining & fine-tuning techniques like LORA, QLORA, RLHF

Test your knowledge of Text Models & their Model Architectures like BERT, DistilBERT, Roberta, T5 Models & More

Conceptual & Practical Implementation based questions on RAGs, LangChain, Vector Databases & AI Model Compression, Deployment, Distributed Training Techniques

Who Should Attend

Data Scientists

AI Engineers

Machine Learning Engineers

Generative AI Engineers

Computer Science Graduate Students

Python Developers

Target Audiences

Data Scientists

AI Engineers

Machine Learning Engineers

Generative AI Engineers

Computer Science Graduate Students

Python Developers

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 monthto 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

Course Curriculum

Instructors

Advanced Techedu
Instructor | Tech Educator | AI Advances

Rating Distribution

1 stars: 0 votes

2 stars: 0 votes

3 stars: 0 votes

4 stars: 1 votes

5 stars: 2 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!