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[Hardware] Realistic NCA-GENL Latest Test Preparation Provide Prefect Assistance in NCA-GEN

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【Hardware】 Realistic NCA-GENL Latest Test Preparation Provide Prefect Assistance in NCA-GEN

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The NVIDIA NCA-GENL certification topics or syllabus are updated with the passage of time. To pass the NVIDIA NVIDIA Generative AI LLMs exam you have to know these topics. The PassReview NCA-GENL certification exam trainers always work on these topics and add their appropriate NVIDIA NCA-GENL Exam Questions And Answers in the NCA-GENL exam dumps. These latest NVIDIA NVIDIA Generative AI LLMs exam topics are added in all NVIDIA NVIDIA Generative AI LLMs exam questions formats.
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NVIDIA NCA-GENL Exam Syllabus Topics:
TopicDetails
Topic 1
  • Python Libraries for LLMs: This section of the exam measures skills of LLM Developers and covers using Python tools and frameworks like Hugging Face Transformers, LangChain, and PyTorch to build, fine-tune, and deploy large language models. It focuses on practical implementation and ecosystem familiarity.
Topic 2
  • Alignment: This section of the exam measures the skills of AI Policy Engineers and covers techniques to align LLM outputs with human intentions and values. It includes safety mechanisms, ethical safeguards, and tuning strategies to reduce harmful, biased, or inaccurate results from models.
Topic 3
  • LLM Integration and Deployment: This section of the exam measures skills of AI Platform Engineers and covers connecting LLMs with applications or services through APIs, and deploying them securely and efficiently at scale. It also includes considerations for latency, cost, monitoring, and updates in production environments.
Topic 4
  • Data Analysis and Visualization: This section of the exam measures the skills of Data Scientists and covers interpreting, cleaning, and presenting data through visual storytelling. It emphasizes how to use visualization to extract insights and evaluate model behavior, performance, or training data patterns.
Topic 5
  • Data Preprocessing and Feature Engineering: This section of the exam measures the skills of Data Engineers and covers preparing raw data into usable formats for model training or fine-tuning. It includes cleaning, normalizing, tokenizing, and feature extraction methods essential to building robust LLM pipelines.

NVIDIA Generative AI LLMs Sample Questions (Q78-Q83):NEW QUESTION # 78
Which Python library is specifically designed for working with large language models (LLMs)?
  • A. Pandas
  • B. NumPy
  • C. Scikit-learn
  • D. HuggingFace Transformers
Answer: D
Explanation:
The HuggingFace Transformers library is specifically designed for working with large language models (LLMs), providing tools for model training, fine-tuning, and inference with transformer-based architectures (e.
g., BERT, GPT, T5). NVIDIA's NeMo documentation often references HuggingFace Transformers for NLP tasks, as it supports integration with NVIDIA GPUs and frameworks like PyTorch for optimized performance.
Option A (NumPy) is for numerical computations, not LLMs. Option B (Pandas) is for data manipulation, not model-specific tasks. Option D (Scikit-learn) is for traditional machine learning, not transformer-based LLMs.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplear ... /docs/en/stable/nlp
/intro.html
HuggingFace Transformers Documentation: https://huggingface.co/docs/transformers/index

NEW QUESTION # 79
Which of the following optimizations are provided by TensorRT? (Choose two.)
  • A. Variable learning rate
  • B. Multi-Stream Execution
  • C. Residual connections
  • D. Layer Fusion
  • E. Data augmentation
Answer: B,D
Explanation:
NVIDIA TensorRT provides optimizations to enhance the performance of deep learning models during inference, as detailed in NVIDIA's Generative AI and LLMs course. Two key optimizations are multi-stream execution and layer fusion. Multi-stream execution allows parallel processing of multiple input streams on the GPU, improving throughput for concurrent inference tasks. Layer fusion combines multiple layers of a neural network (e.g., convolution and activation) into a single operation, reducing memory access and computation time. Option A, data augmentation, is incorrect, as it is a preprocessing technique, not a TensorRT optimization. Option B, variable learning rate, is a training technique, not relevant to inference. Option E, residual connections, is a model architecture feature, not a TensorRT optimization. The course states:
"TensorRT optimizes inference through techniques like layer fusion, which combines operations to reduce overhead, and multi-stream execution, which enables parallel processing for higher throughput." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 80
What is the purpose of the NVIDIA NGC catalog?
  • A. To provide a marketplace for buying and selling software development tools and resources.
  • B. To provide a curated collection of GPU-optimized AI and data science software.
  • C. To provide a platform for testing and debugging software applications.
  • D. To provide a platform for developers to collaborate and share software development projects.
Answer: B
Explanation:
The NVIDIA NGC catalog is a curated repository of GPU-optimized software for AI, machine learning, and data science, as highlighted in NVIDIA's Generative AI and LLMs course. It provides developers with pre- built containers, pre-trained models, and tools optimized for NVIDIA GPUs, enabling faster development and deployment of AI solutions, including LLMs. These resources are designed to streamline workflows and ensure compatibility with NVIDIA hardware. Option A is incorrect, as NGC is not primarily for testing or debugging but for providing optimized software. Option B is wrong, as it is not a collaboration platform like GitHub. Option C is inaccurate, as NGC is not a marketplace for buying and selling but a free resource hub.
The course notes: "The NVIDIA NGC catalog offers a curated collection of GPU-optimized AI and data science software, including containers and models, to accelerate development and deployment." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA NeMo Framework User Guide.

NEW QUESTION # 81
What are some methods to overcome limited throughput between CPU and GPU? (Pick the 2 correct responses)
  • A. Increase the clock speed of the CPU.
  • B. Using techniques like memory pooling.
  • C. Upgrade the GPU to a higher-end model.
  • D. Increase the number of CPU cores.
Answer: B,C
Explanation:
Limited throughput between CPU and GPU often results from data transfer bottlenecks or inefficient resource utilization. NVIDIA's documentation on optimizing deep learning workflows (e.g., using CUDA and cuDNN) suggests the following:
* Option B: Memory pooling techniques, such as pinned memory or unified memory, reduce data transfer overhead by optimizing how data is staged between CPU and GPU.
References:
NVIDIA CUDA Documentation: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html NVIDIA GPU Product Documentation:https://www.nvidia.com/en-us/data-center/products/

NEW QUESTION # 82
Which metric is commonly used to evaluate machine-translation models?
  • A. ROUGE score
  • B. BLEU score
  • C. F1 Score
  • D. Perplexity
Answer: A
Explanation:
The BLEU (Bilingual Evaluation Understudy) score is the most commonly used metric for evaluating machine-translation models. It measures the precision of n-gram overlaps between the generated translation and reference translations, providing a quantitative measure of translation quality. NVIDIA's NeMo documentation on NLP tasks, particularly machine translation, highlights BLEU as the standard metric for assessing translation performance due to its focus on precision and fluency. Option A (F1 Score) is used for classification tasks, not translation. Option C (ROUGE) is primarily for summarization, focusing on recall.
Option D (Perplexity) measures language model quality but is less specific to translation evaluation.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplear ... /docs/en/stable/nlp
/intro.html
Papineni, K., et al. (2002). "BLEU: A Method for Automatic Evaluation of Machine Translation."

NEW QUESTION # 83
......
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