NCA-GENL Dumps Updated to V9.02: The Latest Study Resource for NVIDIA Generative AI LLMs Exam Preparation

1. Which aspect in the development of ethical AI systems ensures they align with societal values and norms?

2. Which tool would you use to select training data with specific keywords?

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

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

5. 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?

6. You have developed a deep learning model for a recommendation system. You want to evaluate the

performance of the model using A/B testing.

What is the rationale for using A/B testing with deep learning model performance?

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

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

9. Which of the following is a key characteristic of Rapid Application Development (RAD)?

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

11. What is a Tokenizer in Large Language Models (LLM)?

12. What is the main difference between forward diffusion and reverse diffusion in diffusion models of Generative AI?

13. 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)

14. 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?

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

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

17. Which metric is commonly used to evaluate machine-translation models?

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

19. Which principle of Trustworthy AI primarily concerns the ethical implications of AI's impact on society and includes considerations for both potential misuse and unintended consequences?

20. You have access to training data but no access to test data.

What evaluation method can you use to assess the performance of your AI model?

21. What is Retrieval Augmented Generation (RAG)?

22. Which model deployment framework is used to deploy an NLP project, especially for high-performance inference in production environments?

23. When comparing and contrasting the ReLU and sigmoid activation functions, which statement is true?

24. 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?

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

26. In neural networks, the vanishing gradient problem refers to what problem or issue?

27. What distinguishes BLEU scores from ROUGE scores when evaluating natural language processing models?

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

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

30. What are the main advantages of instructed large language models over traditional, small language models (< 300M parameters)? (Pick the 2 correct responses)

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

32. Which of the following best describes the purpose of attention mechanisms in transformer models?

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

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

35. In the context of machine learning model deployment, how can Docker be utilized to enhance the process?