NVIDIA HIGH-QUALITY NCA-GENL LATEST DUMPS BOOK–PASS NCA-GENL FIRST ATTEMPT

NVIDIA High-quality NCA-GENL Latest Dumps Book–Pass NCA-GENL First Attempt

NVIDIA High-quality NCA-GENL Latest Dumps Book–Pass NCA-GENL First Attempt

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NVIDIA Generative AI LLMs Sample Questions (Q36-Q41):

NEW QUESTION # 36
In neural networks, the vanishing gradient problem refers to what problem or issue?

  • A. The issue of gradients becoming too small during backpropagation, resulting in slow convergence or stagnation of the training process.
  • B. The issue of gradients becoming too large during backpropagation, leading to unstable training.
  • C. The problem of overfitting in neural networks, where the model performs well on the trainingdata but poorly on new, unseen data.
  • D. The problem of underfitting in neural networks, where the model fails to capture the underlying patterns in the data.

Answer: A

Explanation:
The vanishing gradient problem occurs in deep neural networks when gradients become too small during backpropagation, causing slow convergence or stagnation in training, particularly in deeper layers. NVIDIA's documentation on deep learning fundamentals, such as in CUDA and cuDNN guides, explains that this issue is common in architectures like RNNs or deep feedforward networks with certain activation functions (e.g., sigmoid). Techniques like ReLU activation, batch normalization, or residual connections (used in transformers) mitigate this problem. Option A (overfitting) is unrelated to gradients. Option B describes the exploding gradient problem, not vanishing gradients. Option C (underfitting) is a performance issue, not a gradient-related problem.
References:
NVIDIA CUDA Documentation: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html Goodfellow, I., et al. (2016). "Deep Learning." MIT Press.


NEW QUESTION # 37
Transformers are useful for language modeling because their architecture is uniquely suited for handling which of the following?

  • A. Long sequences
  • B. Translations
  • C. Class tokens
  • D. Embeddings

Answer: A

Explanation:
The transformer architecture, introduced in "Attention is All You Need" (Vaswani et al., 2017), is particularly effective for language modeling due to its ability to handle long sequences. Unlike RNNs, which struggle with long-term dependencies due to sequential processing, transformers use self-attention mechanisms to process all tokens in a sequence simultaneously, capturing relationships across long distances. NVIDIA's NeMo documentation emphasizes that transformers excel in tasks like language modeling because their attention mechanisms scale well with sequence length, especially with optimizations like sparse attention or efficient attention variants. Option B (embeddings) is a component, not a unique strength. Option C (class tokens) is specific to certain models like BERT, not a general transformer feature. Option D (translations) is an application, not a structural advantage.
References:
Vaswani, A., et al. (2017). "Attention is All You Need."
NVIDIA NeMo Documentation:https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html


NEW QUESTION # 38
When fine-tuning an LLM for a specific application, why is it essential to perform exploratory data analysis (EDA) on the new training dataset?

  • A. To select the appropriate learning rate for the model
  • B. To assess the computing resources required for fine-tuning
  • C. To uncover patterns and anomalies in the dataset
  • D. To determine the optimum number of layers in the neural network

Answer: C

Explanation:
Exploratory Data Analysis (EDA) is a critical step in fine-tuning large language models (LLMs) to understand the characteristics of the new training dataset. NVIDIA's NeMo documentation on data preprocessing for NLP tasks emphasizes that EDA helps uncover patterns (e.g., class distributions, word frequencies) and anomalies (e.g., outliers, missing values) that can affect model performance. For example, EDA might reveal imbalanced classes or noisy data, prompting preprocessing steps like data cleaning or augmentation. Option B is incorrect, as learning rate selection is part of model training, not EDA. Option C is unrelated, as EDA does not assess computational resources. Option D is false, as the number of layers is a model architecture decision, not derived from EDA.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html


NEW QUESTION # 39
What is Retrieval Augmented Generation (RAG)?

  • A. RAG is an architecture used to optimize the output of an LLM by retraining the model with domain- specific data.
  • B. RAG is a methodology that combines an information retrieval component with a response generator.
  • C. RAG is a technique used to fine-tune pre-trained LLMs for improved performance.
  • D. RAG is a method for manipulating and generating text-based data using Transformer-based LLMs.

Answer: B

Explanation:
Retrieval-Augmented Generation (RAG) is a methodology that enhances the performance of large language models (LLMs) by integrating an information retrieval component with a generative model. As described in the seminal paper by Lewis et al. (2020), RAG retrieves relevant documents from an external knowledge base (e.g., using dense vector representations) and uses them to inform the generative process, enabling more accurate and contextually relevant responses. NVIDIA's documentation on generative AI workflows, particularly in the context of NeMo and Triton Inference Server, highlights RAG as a technique to improve LLM outputs by grounding them in external data, especially for tasks requiring factual accuracy or domain- specific knowledge. OptionA is incorrect because RAG does not involve retraining the model but rather augments it with retrieved data. Option C is too vague and does not capture the retrieval aspect, while Option D refers to fine-tuning, which is a separate process.
References:
Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html


NEW QUESTION # 40
In the development of trustworthy AI systems, what is the primary purpose of implementing red-teaming exercises during the alignment process of large language models?

  • A. To increase the model's parameter count for better performance.
  • B. To automate the collection of training data for fine-tuning.
  • C. To identify and mitigate potential biases, safety risks, and harmful outputs.
  • D. To optimize the model's inference speed for production deployment.

Answer: C

Explanation:
Red-teaming exercises involve systematically testing a large language model (LLM) by probing it with adversarial or challenging inputs to uncover vulnerabilities, such as biases, unsafe responses, or harmful outputs. NVIDIA's Trustworthy AI framework emphasizes red-teaming as a critical stepin the alignment process to ensure LLMs adhere to ethical standards and societal values. By simulating worst-case scenarios, red-teaming helps developers identify and mitigate risks, such as generating toxic content or reinforcing stereotypes, before deployment. Option A is incorrect, as red-teaming focuses on safety, not speed. Option C is false, as it does not involve model size. Option D is wrong, as red-teaming is about evaluation, not data collection.
References:
NVIDIA Trustworthy AI: https://www.nvidia.com/en-us/ai-data-science/trustworthy-ai/


NEW QUESTION # 41
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