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NVIDIA Generative AI LLMs Sample Questions (Q46-Q51):

NEW QUESTION # 46
What are some methods to overcome limited throughput between CPU and GPU? (Pick the 2 correct responses)

A. Upgrade the GPU to a higher-end model.
B. Using techniques like memory pooling.
C. Increase the clock speed of the CPU.
D. Increase the number of CPU cores.

Answer: A,B

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 # 47
Which of the following is a key characteristic of Rapid Application Development (RAD)?

A. Minimal user feedback during the development process.
B. Iterative prototyping with active user involvement.
C. Linear progression through predefined project phases.
D. Extensive upfront planning before any development.

Answer: B

Explanation:
Rapid Application Development (RAD) is a software development methodology that emphasizes iterative prototyping and active user involvement to accelerate development and ensure alignment with user needs.
NVIDIA's documentation on AI application development, particularly in the context of NGC (NVIDIA GPU Cloud) and software workflows, aligns with RAD principles for quickly building and iterating on AI-driven applications. RAD involves creating prototypes, gathering user feedback, and refining the application iteratively, unlike traditional waterfall models. Option B is incorrect, as RAD minimizes upfront planning in favor of flexibility. Option C describes a linear waterfall approach, not RAD. Option D is false, as RAD relies heavily on user feedback.
References:
NVIDIA NGC Documentation: https://docs.nvidia.com/ngc/ngc-overview/index.html

NEW QUESTION # 48
Why do we need positional encoding in transformer-based models?

A. To prevent overfitting of the model.
B. To represent the order of elements in a sequence.
C. To reduce the dimensionality of the input data.
D. To increase the throughput of the model.

Answer: B

Explanation:
Positional encoding is a critical component in transformer-based models because, unlike recurrent neural networks (RNNs), transformers process input sequences in parallel and lack an inherent sense of word order.
Positional encoding addresses this by embedding information about the position of each token in the sequence, enabling the model to understand the sequential relationships between tokens. According to the original transformer paper ("Attention is All You Need" by Vaswani et al., 2017), positional encodings are added to the input embeddings to provide the model with information about the relative or absolute position of tokens. NVIDIA's documentation on transformer-based models, such as those supported by the NeMo framework, emphasizes that positional encodings are typically implemented using sinusoidal functions or learned embeddings to preserve sequence order, which is essential for tasks like natural language processing (NLP). Options B, C, and D are incorrect because positional encoding does not address overfitting, dimensionality reduction, or throughput directly; these are handled by other techniques like regularization, dimensionality reduction methods, or hardware optimization.
References:
Vaswani, A., et al. (2017). "Attention is All You Need."
NVIDIA NeMo Documentation:https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html

NEW QUESTION # 49
What is a Tokenizer in Large Language Models (LLM)?

A. A tool used to split text into smaller units called tokens for analysis and processing.
B. A technique used to convert text data into numerical representations called tokens for machine learning.
C. A machine learning algorithm that predicts the next word/token in a sequence of text.
D. A method to remove stop words and punctuation marks from text data.

Answer: A

Explanation:
A tokenizer in the context of large language models (LLMs) is a tool that splits text into smaller units called tokens (e.g., words, subwords, or characters) for processing by the model. NVIDIA's NeMo documentation on NLP preprocessing explains that tokenization is a critical step in preparing text data, with algorithms like WordPiece, Byte-Pair Encoding (BPE), or SentencePiece breaking text into manageable units to handle vocabulary constraints and out-of-vocabulary words. For example, the sentence "I love AI" might be tokenized into ["I", "love", "AI"] or subword units like ["I", "lov", "##e", "AI"]. Option A is incorrect, as removing stop words is a separate preprocessing step. Option B is wrong, as tokenization is not a predictive algorithm. Option D is misleading, as converting text to numerical representations is the role of embeddings, not tokenization.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html

NEW QUESTION # 50
In the context of fine-tuning LLMs, which of the following metrics is most commonly used to assess the performance of a fine-tuned model?

A. Training duration
B. Number of layers
C. Accuracy on a validation set
D. Model size

Answer: C

Explanation:
When fine-tuning large language models (LLMs), the primary goal is to improve the model's performance on a specific task. The most common metric for assessing this performance is accuracy on a validation set, as it directly measures how well the model generalizes to unseen data. NVIDIA's NeMo framework documentation for fine-tuning LLMs emphasizes the use of validation metrics such as accuracy, F1 score, or task-specific metrics (e.g., BLEU for translation) to evaluate model performance during and after fine-tuning.
These metrics provide a quantitative measure of the model's effectiveness on the target task. Options A, C, and D (model size, training duration, and number of layers) are not performance metrics; they are either architectural characteristics or training parameters that do not directly reflect the model's effectiveness.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/model_finetuning.html

NEW QUESTION # 51
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Pass Guaranteed Quiz 2025 NVIDIA The Best NCA-GENL: NVIDIA Generative AI LLMs Books PDF

Is Using NVIDIA NCA-GENL Exam Dumps Important To Pass The Exam?

NVIDIA Generative AI LLMs Sample Questions (Q46-Q51):

NCA-GENL Books PDF: 2025 NVIDIA Realistic NVIDIA Generative AI LLMs Books PDF Pass Guaranteed

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