NCA-GENL Latest Braindumps Questions - NCA-GENL Related Exams

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NVIDIA Generative AI LLMs Sample Questions (Q37-Q42):

NEW QUESTION # 37
What is the fundamental role of LangChain in an LLM workflow?

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

Answer: D

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

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
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?

A. Accountability
B. Legal Responsibility
C. Certification
D. Data Privacy

Answer: A

Explanation:
Accountability is a core principle of Trustworthy AI that addresses the ethical implications of AI's societal impact, including potential misuse and unintended consequences. NVIDIA's guidelines on Trustworthy AI, as outlined in their AI ethics framework, emphasize accountability as ensuring that AI systems are transparent, responsible, and answerable for their outcomes. This includes mitigating risks of bias, ensuring fairness, and addressing unintended societal impacts. Option A (Certification) refers to compliance processes, not ethical implications. Option B (Data Privacy) focuses on protecting user data, not broader societal impact. Option D (Legal Responsibility) is related but narrower, focusing on liability rather than ethical considerations.
References:
NVIDIA Trustworthy AI:https://www.nvidia.com/en-us/ai-data-science/trustworthy-ai/

NEW QUESTION # 40
Which of the following prompt engineering techniques is most effective for improving an LLM's performance on multi-step reasoning tasks?

A. Retrieval-augmented generation without context
B. Few-shot prompting with unrelated examples.
C. Zero-shot prompting with detailed task descriptions.
D. Chain-of-thought prompting with explicit intermediate steps.

Answer: D

Explanation:
Chain-of-thought (CoT) prompting is a highly effective technique for improving large language model (LLM) performance on multi-step reasoning tasks. By including explicit intermediate steps in the prompt, CoT guides the model to break down complex problems into manageable parts, improving reasoning accuracy. NVIDIA's NeMo documentation on prompt engineering highlights CoT as a powerful method for tasks like mathematical reasoning or logical problem-solving, as it leverages the model's ability to follow structured reasoning paths. Option A is incorrect, as retrieval-augmented generation (RAG) without context is less effective for reasoning tasks. Option B is wrong, as unrelated examples in few-shot prompting do not aid reasoning. Option C (zero-shot prompting) is less effective than CoT for complex reasoning.
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 # 41
What is 'chunking' in Retrieval-Augmented Generation (RAG)?

A. A method used in RAG to generate random text.
B. Rewrite blocks of text to fill a context window.
C. A technique used in RAG to split text into meaningful segments.
D. A concept in RAG that refers to the training of large language models.

Answer: C

Explanation:
Chunking in Retrieval-Augmented Generation (RAG) refers to the process of splitting large text documents into smaller, meaningful segments (or chunks) to facilitate efficient retrieval and processing by the LLM.
According to NVIDIA's documentation on RAG workflows (e.g., in NeMo and Triton), chunking ensures that retrieved text fits within the model's context window and is relevant to the query, improving the quality of generated responses. For example, a long document might be divided into paragraphs or sentences to allow the retrieval component to select only the most pertinent chunks. Option A is incorrect because chunking does not involve rewriting text. Option B is wrong, as chunking is not about generating random text. Option C is unrelated, as chunking is not a training process.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks."

NEW QUESTION # 42
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NCA-GENL LATEST BRAINDUMPS QUESTIONS - NCA-GENL RELATED EXAMS

NCA-GENL Latest Braindumps Questions - NCA-GENL Related Exams