Nvidia人工智能面经_Nvidia人工智能面试真题及高频题

1.MLLM Design Based on Resume

2.ML Design for Tuning Pretrained LLM

3.ML Design for LLM

4.AI Knowledge: Deep Dive into Resume Topics

5.Paper Review and Discussion

6.Machine Learning Domain Knowledge Evaluation

7.NLP Coding Challenge

8.ML and NLP Domain Knowledge Assessment

9.Discuss deep learning experience and behavioral questions

10.Explain working principles of CNN, RNN, Transformer

11.Impact of using dropout during training but not during inference/testing

12.Deep Learning Frameworks and Pipeline Parallelism

1. MLLM Design Based on Resume

Discuss how to continue pretraining for an MLLM design based on your resume, and answer questions related to your knowledge of audio, video, and ML ops.

2. ML Design for Tuning Pretrained LLM

Given only a few SFT data points and a pretrained vision backbone, explain how you would tune a pretrained LLM to become a tuned Vision Language Model (VLM).

3. ML Design for LLM

Describe how you would design a machine learning system for a Language Learning Model (LLM) that includes the ability to understand images and dialogues, and eventually convert dialogues into speech.

4. AI Knowledge: Deep Dive into Resume Topics

Discuss in-depth topics from your resume related to AI, such as NLP/BERT, accelerating GEMM, and basic knowledge of LLM and transformers. How would you accelerate convolution operations?

5. Paper Review and Discussion

For the third round, you may be asked to review a paper and prepare for questions regarding it. Ensure you have thoroughly read and understood the paper, as it could be a topic of discussion during the interview.