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Practice NCA-GENL Exams & NCA-GENL Valid Braindumps Sheet

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NVIDIA Generative AI LLMs Sample Questions (Q41-Q46):

NEW QUESTION # 41
In the context of preparing a multilingual dataset for fine-tuning an LLM, which preprocessing technique is most effective for handling text from diverse scripts (e.g., Latin, Cyrillic, Devanagari) to ensure consistent model performance?

A. Applying Unicode normalization to standardize character encodings.
B. Removing all non-Latin characters to simplify the input.
C. Converting text to phonetic representations for cross-lingual alignment.
D. Normalizing all text to a single script using transliteration.

Answer: A

Explanation:
When preparing a multilingual dataset for fine-tuning an LLM, applying Unicode normalization (e.g., NFKC or NFC forms) is the most effective preprocessing technique to handle text from diverse scripts like Latin, Cyrillic, or Devanagari. Unicode normalization standardizes character encodings, ensuring that visually identical characters (e.g., precomposed vs. decomposed forms) are represented consistently, which improves model performance across languages. NVIDIA's NeMo documentation on multilingual NLP preprocessing recommends Unicode normalization to address encoding inconsistencies in diverse datasets. Option A (transliteration) may lose linguistic nuances. Option C (removing non-Latin characters) discards critical information. Option D (phonetic conversion) is impractical for text-based LLMs.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

NEW QUESTION # 42
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. Certification
B. Accountability
C. Data Privacy
D. Legal Responsibility

Answer: B

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 # 43
In transformer-based LLMs, how does the use of multi-head attention improve model performance compared to single-head attention, particularly for complex NLP tasks?

A. Multi-head attention allows the model to focus on multiple aspects of the input sequence simultaneously.
B. Multi-head attention eliminates the need for positional encodings in the input sequence.
C. Multi-head attention reduces the model's memory footprint by sharing weights across heads.
D. Multi-head attention simplifies the training process by reducing the number of parameters.

Answer: A

Explanation:
Multi-head attention, a core component of the transformer architecture, improves model performance by allowing the model to attend to multiple aspects of the input sequence simultaneously. Each attention head learns to focus on different relationships (e.g., syntactic, semantic) in the input, capturing diverse contextual dependencies. According to "Attention is All You Need" (Vaswani et al., 2017) and NVIDIA's NeMo documentation, multi-head attention enhances the expressive power of transformers, making them highly effective for complex NLP tasks like translation or question-answering. Option A is incorrect, as multi-head attention increases memory usage. Option C is false, as positional encodings are still required. Option D is wrong, asmulti-head attention adds parameters.
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 # 44
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 uses bottom-up processing, while reverse diffusion uses top-down processing to generate samples from noise vectors.
C. Forward diffusion uses feed-forward networks, while reverse diffusion uses recurrent networks.
D. 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.

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 # 45
Which of the following prompt engineering techniques is most effective for improving an LLM's performance on multi-step reasoning tasks?

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

Answer: C

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 # 46
......

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