DOWNLOAD UPDATED NVIDIA NCA-GENL DUMPS AT DISCOUNT AND START PREPARATION TODAY

Download Updated NVIDIA NCA-GENL Dumps at Discount and Start Preparation Today

Download Updated NVIDIA NCA-GENL Dumps at Discount and Start Preparation Today

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NVIDIA Generative AI LLMs Sample Questions (Q42-Q47):

NEW QUESTION # 42
What is the fundamental role of LangChain in an LLM workflow?

  • A. To reduce the size of AI foundation models.
  • B. To orchestrate LLM components into complex workflows.
  • C. To act as a replacement for traditional programming languages.
  • D. To directly manage the hardware resources used by LLMs.

Answer: B

Explanation:
LangChain is a framework designed to simplify the development of applications powered by large language models (LLMs) by orchestrating various components, such as LLMs, external data sources, memory, and tools, into cohesive workflows. According to NVIDIA's documentation on generative AI workflows, particularly in the context of integrating LLMs with external systems, LangChain enables developers to build complex applications by chaining together prompts, retrieval systems (e.g., for RAG), and memory modules to maintain context across interactions. For example, LangChain can integrate an LLM with a vector database for retrieval-augmented generation or manage conversational history for chatbots. Option A is incorrect, as LangChain complements, not replaces, programming languages. Option B is wrong, as LangChain does not modify model size. Option D is inaccurate, as hardware management is handled by platforms like NVIDIA Triton, not LangChain.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html LangChain Official Documentation: https://python.langchain.com/docs/get_started/introduction


NEW QUESTION # 43
Which tool would you use to select training data with specific keywords?

  • A. Tableau dashboard
  • B. Regular expression filter
  • C. ActionScript
  • D. JSON parser

Answer: B

Explanation:
Regular expression (regex) filters are widely used in data preprocessing to select text data containing specific keywords or patterns. NVIDIA's documentation on data preprocessing for NLP tasks, such as in NeMo, highlights regex as a standard tool for filtering datasets based on textual criteria, enabling efficient data curation. For example, a regex pattern like .*keyword.* can select all texts containing "keyword." Option A (ActionScript) is a programming language for multimedia, not data filtering. Option B (Tableau) is for visualization, not text filtering. Option C (JSON parser) is for structured data, not keyword-based text selection.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html


NEW QUESTION # 44
What is a Tokenizer in Large Language Models (LLM)?

  • A. A method to remove stop words and punctuation marks from text data.
  • B. A technique used to convert text data into numerical representations called tokens for machine learning.
  • C. A tool used to split text into smaller units called tokens for analysis and processing.
  • D. A machine learning algorithm that predicts the next word/token in a sequence of text.

Answer: C

Explanation:
A tokenizer in the context of large language models (LLMs) is a tool that splits text into smaller units called tokens (e.g., words, subwords, or characters) for processing by the model. NVIDIA's NeMo documentation on NLP preprocessing explains that tokenization is a critical step in preparing text data, with algorithms like WordPiece, Byte-Pair Encoding (BPE), or SentencePiece breaking text into manageable units to handle vocabulary constraints and out-of-vocabulary words. For example, the sentence "I love AI" might be tokenized into ["I", "love", "AI"] or subword units like ["I", "lov", "##e", "AI"]. Option A is incorrect, as removing stop words is a separate preprocessing step. Option B is wrong, as tokenization is not a predictive algorithm. Option D is misleading, as converting text to numerical representations is the role of embeddings, not tokenization.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html


NEW QUESTION # 45
Which of the following best describes the purpose of attention mechanisms in transformer models?

  • A. To compress the input sequence for faster processing.
  • B. To convert text into numerical representations.
  • C. To focus on relevant parts of the input sequence for use in the downstream task.
  • D. To generate random noise for improved model robustness.

Answer: C

Explanation:
Attention mechanisms in transformer models, as introduced in "Attention is All You Need" (Vaswani et al.,
2017), allow the model to focus on relevant parts of the input sequence by assigning higher weights to important tokens during processing. NVIDIA's NeMo documentation explains that self-attention enables transformers to capture long-range dependencies and contextual relationships, making them effective for tasks like language modeling and translation. Option B is incorrect, as attention does not compress sequences but processes them fully. Option C is false, as attention is not about generating noise. Option D refers to embeddings, not attention.
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 # 46
What is the main difference between forward diffusion and reverse diffusion in diffusion models of Generative AI?

  • A. Forward diffusion uses feed-forward networks, while reverse diffusion uses recurrent networks.
  • B. Forward diffusion uses bottom-up processing, while reverse diffusion uses top-down processing to generate samples from noise vectors.
  • C. 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.
  • D. Forward diffusion focuses on progressively injecting noise into data, while reverse diffusion focuses on generating new samples from the given noise vectors.

Answer: D

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 # 47
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