NCA-GENL RELIABLE DUMP | PRACTICE NCA-GENL EXAM PDF

NCA-GENL Reliable Dump | Practice NCA-GENL Exam Pdf

NCA-GENL Reliable Dump | Practice NCA-GENL Exam Pdf

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NVIDIA Generative AI LLMs Sample Questions (Q15-Q20):

NEW QUESTION # 15
Transformers are useful for language modeling because their architecture is uniquely suited for handling which of the following?

  • A. Embeddings
  • B. Long sequences
  • C. Class tokens
  • D. Translations

Answer: B

Explanation:
The transformer architecture, introduced in "Attention is All You Need" (Vaswani et al., 2017), is particularly effective for language modeling due to its ability to handle long sequences. Unlike RNNs, which struggle with long-term dependencies due to sequential processing, transformers use self-attention mechanisms to process all tokens in a sequence simultaneously, capturing relationships across long distances. NVIDIA's NeMo documentation emphasizes that transformers excel in tasks like language modeling because their attention mechanisms scale well with sequence length, especially with optimizations like sparse attention or efficient attention variants. Option B (embeddings) is a component, not a unique strength. Option C (class tokens) is specific to certain models like BERT, not a general transformer feature. Option D (translations) is an application, not a structural advantage.
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 # 16
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 focuses on progressively injecting noise into data, while reverse diffusion focuses on generating new samples from the given 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 uses bottom-up processing, while reverse diffusion uses top-down processing to generate samples from noise vectors.

Answer: B

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 # 17
What are the main advantages of instructed large language models over traditional, small language models (<
300M parameters)? (Pick the 2 correct responses)

  • A. Smaller latency, higher throughput.
  • B. Single generic model can do more than one task.
  • C. Cheaper computational costs during inference.
  • D. Trained without the need for labeled data.
  • E. It is easier to explain the predictions.

Answer: B,C

Explanation:
Instructed large language models (LLMs), such as those supported by NVIDIA's NeMo framework, have significant advantages over smaller, traditional models:
* Option D: LLMs often have cheaper computational costs during inference for certain tasks because they can generalize across multiple tasks without requiring task-specific retraining, unlike smaller models that may need separate models per task.
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 # 18
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 # 19
Which of the following best describes the purpose of attention mechanisms in transformer models?

  • A. To convert text into numerical representations.
  • B. To compress the input sequence for faster processing.
  • 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 # 20
......

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