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【General】 Certification NCA-GENL Torrent - Valid NCA-GENL Exam Testking

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NVIDIA Generative AI LLMs Sample Questions (Q37-Q42):NEW QUESTION # 37
Which library is used to accelerate data preparation operations on the GPU?
  • A. XGBoost
  • B. cuGraph
  • C. cuML
  • D. cuDF
Answer: D
Explanation:
cuDF is a GPU-accelerated data manipulation library within the RAPIDS ecosystem, designed to speed up data preparation operations such as filtering, joining, and aggregating large datasets. As highlighted in NVIDIA's Generative AI and LLMs course, cuDF provides pandas-like functionality for data preprocessing but leverages GPU parallelism to achieve significant performance improvements, making it ideal for data science workflows involving large-scale data preparation. Option A, cuML, is incorrect, as it focuses on machine learning algorithms, not data preparation. Option B, XGBoost, is a gradient boosting framework, not a data preparation library. Option D, cuGraph, is used for graph analytics, not general data preparation. The course notes: "RAPIDS cuDF accelerates data preparation operations by enabling GPU-based processing, offering pandas-like functionality with significant speedups for tasks like data filtering and transformation." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 38
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 # 39
What do we usually refer to as generative AI?
  • A. A branch of artificial intelligence that focuses on improving the efficiency of existing models.
  • B. A branch of artificial intelligence that focuses on auto generation of models for classification.
  • C. A branch of artificial intelligence that focuses on analyzing and interpreting existing data.
  • D. A branch of artificial intelligence that focuses on creating models that can generate new and original data.
Answer: D
Explanation:
Generative AI, as covered in NVIDIA's Generative AI and LLMs course, is a branch of artificial intelligence focused on creating models that can generate new and original data, such as text, images, or audio, that resembles the training data. In the context of LLMs, generative AI involves models like GPT that produce coherent text for tasks like text completion, dialogue, or creative writing by learning patterns from large datasets. These models use techniques like autoregressive generation to create novel outputs. Option B is incorrect, as generative AI is not limited to generating classification models but focuses on producing new data. Option C is wrong, as improving model efficiency is a concern of optimization techniques, not generative AI. Option D is inaccurate, as analyzing and interpreting data falls under discriminative AI, not generative AI. The course emphasizes: "Generative AI involves building models that create new content, such as text or images, by learning the underlying distribution of the training data." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 40
Which of the following principles are widely recognized for building trustworthy AI? (Choose two.)
  • A. Nondiscrimination
  • B. Scalability
  • C. Privacy
  • D. Conversational
  • E. Low latency
Answer: A,C
Explanation:
In building Trustworthy AI, privacy and nondiscrimination are widely recognized principles, as emphasized in NVIDIA's Generative AI and LLMs course. Privacy ensures that AI systems protect user data and maintain confidentiality, often through techniques like confidential computing or data anonymization.
Nondiscrimination ensures that AI models avoid biases and treat all groups fairly, mitigating issues like discriminatory outputs. Option A, conversational, is incorrect, as it is a feature of some AI systems, not a Trustworthy AI principle. Option B, low latency, is a performance goal, not a trust principle. Option D, scalability, is a technical consideration, not directly related to trustworthiness. The course states: "Trustworthy AI principles include privacy, ensuring data protection, and nondiscrimination, ensuring fair and unbiased model behavior, critical for ethical AI development." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 41
When deploying an LLM using NVIDIA Triton Inference Server for a real-time chatbot application, which optimization technique is most effective for reducing latency while maintaining high throughput?
  • A. Increasing the model's parameter count to improve response quality.
  • B. Switching to a CPU-based inference engine for better scalability.
  • C. Enabling dynamic batching to process multiple requests simultaneously.
  • D. Reducing the input sequence length to minimize token processing.
Answer: C
Explanation:
NVIDIA Triton Inference Server is designed for high-performance model deployment, and dynamicbatching is a key optimization technique for reducing latency while maintaining high throughput in real-time applications like chatbots. Dynamic batching groups multiple inference requests into a single batch, leveraging GPU parallelism to process them simultaneously, thus reducing per-request latency. According to NVIDIA's Triton documentation, this is particularly effective for LLMs with variable input sizes, as it maximizes resource utilization. Option A is incorrect, as increasing parameters increases latency. Option C may reduce latency but sacrifices context and quality. Option D is false, as CPU-based inference is slower than GPU-based for LLMs.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server
/user-guide/docs/index.html

NEW QUESTION # 42
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