NVIDIA LATEST BRAINDUMPS NCA-GENL EBOOK: NVIDIA GENERATIVE AI LLMS - EXAMS4COLLECTION FAST DOWNLOAD

NVIDIA Latest Braindumps NCA-GENL Ebook: NVIDIA Generative AI LLMs - Exams4Collection Fast Download

NVIDIA Latest Braindumps NCA-GENL Ebook: NVIDIA Generative AI LLMs - Exams4Collection Fast Download

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NVIDIA Generative AI LLMs Sample Questions (Q25-Q30):

NEW QUESTION # 25
When designing prompts for a large language model to perform a complex reasoning task, such as solving a multi-step mathematical problem, which advanced prompt engineering technique is most effective in ensuring robust performance across diverse inputs?

  • A. Retrieval-augmented generation with external mathematical databases.
  • B. Chain-of-thought prompting with step-by-step reasoning examples.
  • C. Few-shot prompting with randomly selected examples.
  • D. Zero-shot prompting with a generic task description.

Answer: B

Explanation:
Chain-of-thought (CoT) prompting is an advanced prompt engineering technique that significantly enhances a large language model's (LLM) performance on complex reasoning tasks, such as multi-step mathematical problems. By including examples that explicitly demonstrate step-by-step reasoning in the prompt, CoT guides the model to break down the problem into intermediate steps, improving accuracy and robustness.
NVIDIA's NeMo documentation on prompt engineering highlights CoT as a powerful method for tasks requiring logical or sequential reasoning, as it leverages the model's ability to mimic structured problem- solving. Research by Wei et al. (2022) demonstrates that CoT outperforms other methods for mathematical reasoning. Option A (zero-shot) is less effective for complex tasks due to lack of guidance. Option B (few- shot with random examples) is suboptimal without structured reasoning. Option D (RAG) is useful for factual queries but less relevant for pure reasoning tasks.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html Wei, J., et al. (2022). "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models."


NEW QUESTION # 26
Why do we need positional encoding in transformer-based models?

  • A. To increase the throughput of the model.
  • B. To reduce the dimensionality of the input data.
  • C. To represent the order of elements in a sequence.
  • D. To prevent overfitting of the model.

Answer: C

Explanation:
Positional encoding is a critical component in transformer-based models because, unlike recurrent neural networks (RNNs), transformers process input sequences in parallel and lack an inherent sense of word order.
Positional encoding addresses this by embedding information about the position of each token in the sequence, enabling the model to understand the sequential relationships between tokens. According to the original transformer paper ("Attention is All You Need" by Vaswani et al., 2017), positional encodings are added to the input embeddings to provide the model with information about the relative or absolute position of tokens. NVIDIA's documentation on transformer-based models, such as those supported by the NeMo framework, emphasizes that positional encodings are typically implemented using sinusoidal functions or learned embeddings to preserve sequence order, which is essential for tasks like natural language processing (NLP). Options B, C, and D are incorrect because positional encoding does not address overfitting, dimensionality reduction, or throughput directly; these are handled by other techniques like regularization, dimensionality reduction methods, or hardware optimization.
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 # 27
In the context of evaluating a fine-tuned LLM for a text classification task, which experimental design technique ensures robust performance estimation when dealing with imbalanced datasets?

  • A. Bootstrapping with random sampling.
  • B. Single hold-out validation with a fixed test set.
  • C. Stratified k-fold cross-validation.
  • D. Grid search for hyperparameter tuning.

Answer: C

Explanation:
Stratified k-fold cross-validation is a robust experimental design technique for evaluating machine learning models, especially on imbalanced datasets. It divides the dataset into k folds while preserving the class distribution in each fold, ensuring that the model is evaluated on representative samples of all classes.
NVIDIA's NeMo documentation on model evaluation recommends stratified cross-validation for tasks like text classification to obtain reliable performance estimates, particularly when classes are unevenly distributed (e.g., in sentiment analysis with few negative samples). Option A (single hold-out) is less robust, as it may not capture class imbalance. Option C (bootstrapping) introduces variability and is less suitable for imbalanced data. Option D (grid search) is for hyperparameter tuning, not performance estimation.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/model_finetuning.html


NEW QUESTION # 28
Which technology will allow you to deploy an LLM for production application?

  • A. Pandas
  • B. Falcon
  • C. Triton
  • D. Git

Answer: C

Explanation:
NVIDIA Triton Inference Server is a technology specifically designed for deploying machine learning models, including large language models (LLMs), in production environments. It supports high-performance inference, model management, and scalability across GPUs, making it ideal for real-time LLM applications.
According to NVIDIA's Triton Inference Server documentation, it supports frameworks like PyTorch and TensorFlow, enabling efficient deployment of LLMs with features like dynamic batching and model ensemble. Option A (Git) is a version control system, not a deployment tool. Option B (Pandas) is a data analysis library, irrelevant to model deployment. Option C (Falcon) refers to a specific LLM, not a deployment platform.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server
/user-guide/docs/index.html


NEW QUESTION # 29
What is the main difference between forward diffusion and reverse diffusion in diffusion models of Generative AI?

  • A. Forward diffusion focuses on progressively injecting noise into data, while reverse diffusion focuses on generating new samples from the given noise vectors.
  • B. Forward diffusion focuses on generating a sample from a given noise vector, while reverse diffusion reverses the process by estimating the latent space representation of a given sample.
  • C. Forward diffusion uses bottom-up processing, while reverse diffusion uses top-down processing to generate samples from noise vectors.
  • D. Forward diffusion uses feed-forward networks, while reverse diffusion uses recurrent networks.

Answer: A

Explanation:
Diffusion models, a class of generative AI models, operate in two phases: forward diffusion and reverse diffusion. According to NVIDIA's documentation on generative AI (e.g., in the context of NVIDIA's work on generative models), forward diffusion progressively injects noise into a data sample (e.g., an image or text embedding) over multiple steps, transforming it into a noise distribution. Reverse diffusion, conversely, starts with a noise vector and iteratively denoises it to generate a new sample that resembles the training data distribution. This process is central tomodels like DDPM (Denoising Diffusion Probabilistic Models). Option A is incorrect, as forward diffusion adds noise, not generates samples. Option B is false, as diffusion models typically use convolutional or transformer-based architectures, not recurrent networks. Option C is misleading, as diffusion does not align with bottom-up/top-down processing paradigms.
References:
NVIDIA Generative AI Documentation: https://www.nvidia.com/en-us/ai-data-science/generative-ai/ Ho, J., et al. (2020). "Denoising Diffusion Probabilistic Models."


NEW QUESTION # 30
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