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[General] NCA-GENL Valid Torrent - Provide Valid Material to pass NVIDIA Generative AI LLM

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【General】 NCA-GENL Valid Torrent - Provide Valid Material to pass NVIDIA Generative AI LLM

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NVIDIA Generative AI LLMs Sample Questions (Q88-Q93):NEW QUESTION # 88
What is Retrieval Augmented Generation (RAG)?
  • A. RAG is an architecture used to optimize the output of an LLM by retraining the model with domain- specific data.
  • B. RAG is a methodology that combines an information retrieval component with a response generator.
  • C. RAG is a method for manipulating and generating text-based data using Transformer-based LLMs.
  • D. RAG is a technique used to fine-tune pre-trained LLMs for improved performance.
Answer: B
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. Option A 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/deeplear ... /docs/en/stable/nlp
/intro.html

NEW QUESTION # 89
What is the correct order of steps in an ML project?
  • A. Model evaluation, Data preprocessing, Model training, Data collection
  • B. Data collection, Data preprocessing, Model training, Model evaluation
  • C. Data preprocessing, Data collection, Model training, Model evaluation
  • D. Model evaluation, Data collection, Data preprocessing, Model training
Answer: B
Explanation:
The correct order of steps in a machine learning (ML) project, as outlined in NVIDIA's Generative AI and LLMs course, is: Data collection, Data preprocessing, Model training, and Model evaluation. Data collection involves gathering relevant data for the task. Data preprocessing prepares the data by cleaning, transforming, and formatting it (e.g., tokenization for NLP). Model training involves using the preprocessed data to optimize the model's parameters. Model evaluation assesses the trained model's performance using metrics like accuracy or F1-score. This sequence ensures a systematic approach to building effective ML models.
Options A, B, and C are incorrect, as they disrupt this logical flow (e.g., evaluating before training or preprocessing before collecting data is not feasible). The course states: "An ML project follows a structured pipeline: data collection, data preprocessing, model training, and model evaluation, ensuring data is properly prepared and models are rigorously assessed." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 90
Which technique is used in prompt engineering to guide LLMs in generating more accurate and contextually appropriate responses?
  • A. Choosing another model architecture.
  • B. Increasing the model's parameter count.
  • C. Training the model with additional data.
  • D. Leveraging the system message.
Answer: D
Explanation:
Prompt engineering involves designing inputs to guide large language models (LLMs) to produce desired outputs without modifying the model itself. Leveraging the system message is a key technique, where a predefined instruction or context is provided to the LLM to set the tone, role, or constraints for its responses.
NVIDIA's NeMo framework documentation on conversational AI highlights the use of system messages to improve the contextual accuracy of LLMs, especially in dialogue systems or task-specific applications. For instance, a system message like "You are a helpful technical assistant" ensures responses align with the intended role. Options A, B, and C involve model training or architectural changes, which are not part of prompt engineering.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplear ... /docs/en/stable/nlp
/intro.html

NEW QUESTION # 91
What is the Open Neural Network Exchange (ONNX) format used for?
  • A. Reducing training time of neural networks
  • B. Sharing neural network literature
  • C. Representing deep learning models
  • D. Compressing deep learning models
Answer: C
Explanation:
The Open Neural Network Exchange (ONNX) format is an open-standard representation for deep learning models, enabling interoperability across different frameworks, as highlighted in NVIDIA's Generative AI and LLMs course. ONNX allows models trained in frameworks like PyTorch or TensorFlow to be exported and used in other compatible tools for inference or further development, ensuring portability and flexibility.
Option B is incorrect, as ONNX is not designed to reduce training time but to standardize model representation. Option C is wrong, as model compression is handled by techniques like quantization, not ONNX. Option D is inaccurate, as ONNX is unrelated to sharing literature. The course states: "ONNX is an open format for representing deep learning models, enabling seamless model exchange and deployment across various frameworks and platforms." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 92
When implementing data parallel training, which of the following considerations needs to be taken into account?
  • A. A ring all-reduce is an efficient algorithm for syncing the weights across different processes/devices.
  • B. The model weights are kept independent for as long as possible increasing the model exploration.
  • C. The model weights are synced across all processes/devices only at the end of every epoch.
  • D. A master-worker method for syncing the weights across different processes is desirable due to its scalability.
Answer: A
Explanation:
In data parallel training, where a model is replicated across multiple devices with each processing a portion of the data, synchronizing model weights is critical. As covered in NVIDIA's Generative AI and LLMs course, the ring all-reduce algorithm is an efficient method for syncing weights across processes or devices. It minimizes communication overhead by organizing devices in a ring topology, allowing gradients to be aggregated and shared efficiently. Option A is incorrect, as weights are typically synced after each batch, not just at epoch ends, to ensure consistency. Option B is wrong, as master-worker methods can create bottlenecks and are less scalable than all-reduce. Option D is inaccurate, as keeping weights independent defeats the purpose of data parallelism, which requires synchronized updates. The course notes: "In data parallel training, the ring all-reduce algorithm efficiently synchronizes model weights across devices, reducing communication overhead and ensuring consistent updates." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 93
......
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