NEW NCA-GENL REAL TEST | LATEST NCA-GENL: NVIDIA GENERATIVE AI LLMS 100% PASS

New NCA-GENL Real Test | Latest NCA-GENL: NVIDIA Generative AI LLMs 100% Pass

New NCA-GENL Real Test | Latest NCA-GENL: NVIDIA Generative AI LLMs 100% Pass

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NVIDIA Generative AI LLMs Sample Questions (Q36-Q41):

NEW QUESTION # 36
In the context of machine learning model deployment, how can Docker be utilized to enhance the process?

  • A. To directly increase the accuracy of machine learning models.
  • B. To reduce the computational resources needed for training models.
  • C. To automatically generate features for machine learning models.
  • D. To provide a consistent environment for model training and inference.

Answer: D

Explanation:
Docker is a containerization platform that ensures consistent environments for machine learning model training and inference by packaging dependencies, libraries, and configurations into portable containers.
NVIDIA's documentation on deploying models with Triton Inference Server and NGC (NVIDIA GPU Cloud) emphasizes Docker's role in eliminating environment discrepancies between development and production, ensuring reproducibility. Option A is incorrect, as Docker does not generate features. Option C is false, as Docker does not reduce computational requirements. Option D is wrong, as Docker does not affect model accuracy.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server
/user-guide/docs/index.html
NVIDIA NGC Documentation: https://docs.nvidia.com/ngc/ngc-overview/index.html


NEW QUESTION # 37
In neural networks, the vanishing gradient problem refers to what problem or issue?

  • A. The problem of underfitting in neural networks, where the model fails to capture the underlying patterns in the data.
  • B. The issue of gradients becoming too large during backpropagation, leading to unstable training.
  • C. The issue of gradients becoming too small during backpropagation, resulting in slow convergence or stagnation of the training process.
  • D. The problem of overfitting in neural networks, where the model performs well on the trainingdata but poorly on new, unseen data.

Answer: C

Explanation:
The vanishing gradient problem occurs in deep neural networks when gradients become too small during backpropagation, causing slow convergence or stagnation in training, particularly in deeper layers. NVIDIA's documentation on deep learning fundamentals, such as in CUDA and cuDNN guides, explains that this issue is common in architectures like RNNs or deep feedforward networks with certain activation functions (e.g., sigmoid). Techniques like ReLU activation, batch normalization, or residual connections (used in transformers) mitigate this problem. Option A (overfitting) is unrelated to gradients. Option B describes the exploding gradient problem, not vanishing gradients. Option C (underfitting) is a performance issue, not a gradient-related problem.
References:
NVIDIA CUDA Documentation: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html Goodfellow, I., et al. (2016). "Deep Learning." MIT Press.


NEW QUESTION # 38
Which of the following claims is correct about quantization in the context of Deep Learning? (Pick the 2 correct responses)

  • A. Helps reduce memory requirements and achieve better cache utilization.
  • B. It only involves reducing the number of bits of the parameters.
  • C. It consists of removing a quantity of weights whose values are zero.
  • D. It leads to a substantial loss of model accuracy.
  • E. Quantization might help in saving power and reducing heat production.

Answer: A,E

Explanation:
Quantization in deep learning involves reducing the precision of model weights and activations (e.g., from 32- bit floating-point to 8-bit integers) to optimize performance. According to NVIDIA's documentation on model optimization and deployment (e.g., TensorRT and Triton Inference Server), quantization offers several benefits:
* Option A: Quantization reduces power consumption and heat production by lowering the computational intensity of operations, making it ideal for edge devices.
References:
NVIDIA TensorRT Documentation: https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html


NEW QUESTION # 39
What is 'chunking' in Retrieval-Augmented Generation (RAG)?

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

Answer: A

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 # 40
What is Retrieval Augmented Generation (RAG)?

  • A. RAG is a method for manipulating and generating text-based data using Transformer-based LLMs.
  • B. RAG is a technique used to fine-tune pre-trained LLMs for improved performance.
  • C. RAG is a methodology that combines an information retrieval component with a response generator.
  • D. RAG is an architecture used to optimize the output of an LLM by retraining the model with domain- specific data.

Answer: C

Explanation:
Retrieval-Augmented Generation (RAG) is a methodology that enhances the performance of large language models (LLMs) by integrating an information retrieval component with a generative model. As described in the seminal paper by Lewis et al. (2020), RAG retrieves relevant documents from an external knowledge base (e.g., using dense vector representations) and uses them to inform the generative process, enabling more accurate and contextually relevant responses. NVIDIA's documentation on generative AI workflows, particularly in the context of NeMo and Triton Inference Server, highlights RAG as a technique to improve LLM outputs by grounding them in external data, especially for tasks requiring factual accuracy or domain- specific knowledge. OptionA is incorrect because RAG does not involve retraining the model but rather augments it with retrieved data. Option C is too vague and does not capture the retrieval aspect, while Option D refers to fine-tuning, which is a separate process.
References:
Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html


NEW QUESTION # 41
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