NCA-GENL Exam Vce Format - Pass NCA-GENL Test Guide
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NVIDIA NCA-GENL Exam Syllabus Topics:
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NVIDIA Generative AI LLMs Sample Questions (Q68-Q73):
NEW QUESTION # 68
What is the primary purpose of applying various image transformation techniques (e.g., flipping, rotation, zooming) to a dataset?
Answer: B
Explanation:
Image transformation techniques such as flipping, rotation, and zooming are forms of data augmentation used to artificially increase the size and diversity of a dataset. NVIDIA's Deep Learning AI documentation, particularly for computer vision tasks using frameworks like DALI (Data Loading Library), explains that data augmentation improves a model's ability to generalize by exposing it to varied versions of the training data, thus reducing overfitting. For example, flipping an image horizontally creates a new training sample that helps the model learn invariance to certain transformations. Option A is incorrect because transformations do not simplify the model architecture. Option C is wrong, as augmentation introduces variability, not uniformity. Option D is also incorrect, as augmentation typically increases computational requirements due to additional data processing.
References:
NVIDIA DALI Documentation: https://docs.nvidia.com/deeplearning/dali/user-guide/docs/index.html
NEW QUESTION # 69
Which model deployment framework is used to deploy an NLP project, especially for high-performance inference in production environments?
Answer: B
Explanation:
NVIDIA Triton Inference Server is a high-performance framework designed for deploying machine learning models, including NLP models, in production environments. It supports optimized inference on GPUs, dynamic batching, and integration with frameworks like PyTorch and TensorFlow. According to NVIDIA's Triton documentation, it is ideal for deploying LLMs for real-time applications with low latency. Option A (DeepStream) is for video analytics, not NLP. Option B (HuggingFace) is a library for model development, not deployment. Option C (NeMo) is for training and fine-tuning, not production deployment.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server
/user-guide/docs/index.html
NEW QUESTION # 70
In the field of AI experimentation, what is the GLUE benchmark used to evaluate performance of?
Answer: A
Explanation:
The General Language Understanding Evaluation (GLUE) benchmark is a widely used standard for evaluating AI models on a diverse set of natural language understanding (NLU) tasks, as covered in NVIDIA' s Generative AI and LLMs course. GLUE includes tasks like sentiment analysis, question answering, and textual entailment, designed to test a model's ability to understand and reason about language across multiple domains. It provides a standardized way to compare model performance on NLU. Option A is incorrect, as GLUE does not evaluate speech recognition. Option B is wrong, as it pertains to image recognition, unrelated to GLUE. Option D is inaccurate, as GLUE focuses on NLU, not reinforcement learning. The course states:
"The GLUE benchmark is used to evaluate AI models on a range of natural language understanding tasks, providing a comprehensive assessment of their language processing capabilities." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.
NEW QUESTION # 71
In the context of language models, what does an autoregressive model predict?
Answer: D
Explanation:
Autoregressive models are a cornerstone of modern language modeling, particularly in large language models (LLMs) like those discussed in NVIDIA's Generative AI and LLMs course. These models predict the probability of the next token in a sequence based solely on the preceding tokens, making them inherently sequential and unidirectional. This process is often referred to as "next-token prediction," where the model learns to generate text by estimating the conditional probability distribution of the next token given the context of all previous tokens. For example, given the sequence "The cat is," the model predicts the likelihood of the next word being "on," "in," or another token. This approach is fundamental to models like GPT, which rely on autoregressive decoding to generate coherent text. Unlike bidirectional models (e.g., BERT), which consider both previous and future tokens, autoregressive models focus only on past tokens, making option D incorrect. Options B and C are also inaccurate, as Monte Carlo sampling is not a standard method for next- token prediction in autoregressive models, and the prediction is not limited to recurrent networks or LSTM cells, as modern LLMs often use Transformer architectures. The course emphasizes this concept in the context of Transformer-based NLP: "Learn the basic concepts behind autoregressive generative models, including next-token prediction and its implementation within Transformer-based models." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.
NEW QUESTION # 72
What is the purpose of the NVIDIA NGC catalog?
Answer: C
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 # 73
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