Reliable NCA-GENL Dumps (V8.02) – Stand Out with Verified NVIDIA Generative AI LLMs Exam Questions and Answers

1. Why do we need positional encoding in transformer-based models?

2. What is Retrieval Augmented Generation (RAG)?

3. In the context of fine-tuning LLMs, which of the following metrics is most commonly used to assess the performance of a fine-tuned model?

4. Which of the following claims is correct about quantization in the context of Deep Learning? (Pick the 2 correct responses)

5. What is the primary purpose of applying various image transformation techniques (e.g., flipping, rotation, zooming) to a dataset?

6. Which technique is used in prompt engineering to guide LLMs in generating more accurate and contextually appropriate responses?

7. What are some methods to overcome limited throughput between CPU and GPU? (Pick the 2 correct responses)

8. What is 'chunking' in Retrieval-Augmented Generation (RAG)?

9. How does A/B testing contribute to the optimization of deep learning models' performance and effectiveness in real-world applications? (Pick the 2 correct responses)

10. You are working on developing an application to classify images of animals and need to train a neural model. However, you have a limited amount of labeled data.

Which technique can you use to leverage the knowledge from a model pre-trained on a different task to improve the performance of your new model?

11. What is the fundamental role of LangChain in an LLM workflow?

12. What type of model would you use in emotion classification tasks?

13. In the context of a natural language processing (NLP) application, which approach is most effective for implementing zero-shot learning to classify text data into categories that were not seen during training?

14. Which technology will allow you to deploy an LLM for production application?

15. Which Python library is specifically designed for working with large language models (LLMs)?

16. Transformers are useful for language modeling because their architecture is uniquely suited for handling which of the following?

17. In the context of data preprocessing for Large Language Models (LLMs), what does tokenization refer to?

18. Which calculation is most commonly used to measure the semantic closeness of two text passages?

19. Which of the following contributes to the ability of RAPIDS to accelerate data processing? (Pick the 2 correct responses)