NVIDIA GENERATIVE AI LLMS TORRENT PDF & NCA-GENL FREE DUMPS & NCA-GENL STUDY TORRENT

NVIDIA Generative AI LLMs torrent pdf & NCA-GENL free dumps & NCA-GENL study torrent

NVIDIA Generative AI LLMs torrent pdf & NCA-GENL free dumps & NCA-GENL study torrent

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NVIDIA Generative AI LLMs Sample Questions (Q10-Q15):

NEW QUESTION # 10
What is the purpose of few-shot learning in prompt engineering?

  • A. To fine-tune a model on a massive dataset
  • B. To optimize hyperparameters
  • C. To train a model from scratch
  • D. To give a model some examples

Answer: D

Explanation:
Few-shot learning in prompt engineering involves providing a small number of examples (demonstrations) within the prompt to guide a large language model (LLM) to perform a specific task without modifying its weights. NVIDIA's NeMo documentation on prompt-based learning explains that few-shot prompting leverages the model's pre-trained knowledge by showing it a few input-output pairs, enabling it to generalize to new tasks. For example, providing two examples of sentiment classification in a prompt helps the model understand the task. Option B is incorrect, as few-shot learning does not involve training from scratch. Option C is wrong, as hyperparameter optimization is a separate process. Option D is false, as few-shot learning avoids large-scale fine-tuning.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html
Brown, T., et al. (2020). "Language Models are Few-Shot Learners."


NEW QUESTION # 11
Which of the following is a key characteristic of Rapid Application Development (RAD)?

  • A. Extensive upfront planning before any development.
  • B. Linear progression through predefined project phases.
  • C. Minimal user feedback during the development process.
  • D. Iterative prototyping with active user involvement.

Answer: D

Explanation:
Rapid Application Development (RAD) is a software development methodology that emphasizes iterative prototyping and active user involvement to accelerate development and ensure alignment with user needs.
NVIDIA's documentation on AI application development, particularly in the context of NGC (NVIDIA GPU Cloud) and software workflows, aligns with RAD principles for quickly building and iterating on AI-driven applications. RAD involves creating prototypes, gathering user feedback, and refining the application iteratively, unlike traditional waterfall models. Option B is incorrect, as RAD minimizes upfront planning in favor of flexibility. Option C describes a linear waterfall approach, not RAD. Option D is false, as RAD relies heavily on user feedback.
References:
NVIDIA NGC Documentation: https://docs.nvidia.com/ngc/ngc-overview/index.html


NEW QUESTION # 12
What distinguishes BLEU scores from ROUGE scores when evaluating natural language processing models?

  • A. BLEU scores evaluate the 'precision' of translations, while ROUGE scores focus on the 'recall' of summarized text.
  • B. BLEU scores analyze syntactic structures, while ROUGE scores evaluate semantic accuracy.
  • C. BLEU scores measure model efficiency, whereas ROUGE scores assess computational complexity.
  • D. BLEU scores determine the fluency of text generation, while ROUGE scores rate the uniqueness of generated text.

Answer: A

Explanation:
BLEU (Bilingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) are metrics used to evaluate natural language processing (NLP) models, particularly for tasks like machine translation and text summarization. According to NVIDIA's NeMo documentation on NLP evaluation metrics, BLEU primarily measures the precision of n-gram overlaps between generated and reference translations, making it suitable for assessing translation quality. ROUGE, on the other hand, focuses on recall, measuring the overlap of n-grams, longest common subsequences, or skip-bigrams between generated and reference summaries, making it ideal for summarization tasks. Option A is incorrect, as BLEU and ROUGE do not measure fluency or uniqueness directly. Option B is wrong, as both metrics focus on n-gram overlap, not syntactic or semantic analysis. Option D is false, as neither metric evaluates efficiency or complexity.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html
Papineni, K., et al. (2002). "BLEU: A Method for Automatic Evaluation of Machine Translation." Lin, C.-Y. (2004). "ROUGE: A Package for Automatic Evaluation of Summaries."


NEW QUESTION # 13
Which technology will allow you to deploy an LLM for production application?

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

Answer: A

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 # 14
In the Transformer architecture, which of the following statements about the Q (query), K (key), and V (value) matrices is correct?

  • A. Q represents the query vector used to retrieve relevant information from the input sequence.
  • B. K is responsible for computing the attention scores between the query and key vectors.
  • C. Q, K, and V are randomly initialized weight matrices used for positional encoding.
  • D. V is used to calculate the positional embeddings for each token in the input sequence.

Answer: A

Explanation:
In the transformer architecture, the Q (query), K (key), and V (value) matrices are used in the self-attention mechanism to compute relationships between tokens in a sequence. According to "Attention is All You Need" (Vaswani et al., 2017) and NVIDIA's NeMo documentation, the query vector (Q) represents the token seeking relevant information, the key vector (K) is used to compute compatibility with other tokens, and the value vector (V) provides the information to be retrieved. The attention score is calculated as a scaled dot- product of Q and K, and the output is a weighted sum of V. Option C is correct, as Q retrieves relevant information. Option A is incorrect, as Q, K, and V are not used for positional encoding. Option B is wrong, as attention scores are computed using both Q and K, not K alone. Option D is false, as positional embeddings are separate from V.
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 # 15
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