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NVIDIA Generative AI LLMs Sample Questions (Q22-Q27):

NEW QUESTION # 22
Which Python library is specifically designed for working with large language models (LLMs)?

A. Scikit-learn
B. HuggingFace Transformers
C. NumPy
D. Pandas

Answer: B

Explanation:
The HuggingFace Transformers library is specifically designed for working with large languagemodels (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/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html HuggingFace Transformers Documentation: https://huggingface.co/docs/transformers/index

NEW QUESTION # 23
When preprocessing text data for an LLM fine-tuning task, why is it critical to apply subword tokenization (e.
g., Byte-Pair Encoding) instead of word-based tokenization for handling rare or out-of-vocabulary words?

A. Subword tokenization removes punctuation and special characters to simplify text input.
B. Subword tokenization breaks words into smaller units, enabling the model to generalize to unseen words.
C. Subword tokenization creates a fixed-size vocabulary to prevent memory overflow.
D. Subword tokenization reduces the model's computational complexity by eliminating embeddings.

Answer: B

Explanation:
Subword tokenization, such as Byte-Pair Encoding (BPE) or WordPiece, is critical for preprocessing text data in LLM fine-tuning because it breaks words into smaller units (subwords), enabling the model to handle rare or out-of-vocabulary (OOV) words effectively. NVIDIA's NeMo documentation on tokenization explains that subword tokenization creates a vocabulary of frequent subword units, allowing the model to represent unseen words by combining known subwords (e.g., "unseen" as "un" + "##seen"). This improves generalization compared to word-based tokenization, which struggles with OOV words. Option A is incorrect, as tokenization does not eliminate embeddings. Option B is false, as vocabulary size is not fixed but optimized.
Option D is wrong, as punctuation handling is a separate preprocessing step.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html

NEW QUESTION # 24
In the context of evaluating a fine-tuned LLM for a text classification task, which experimental design technique ensures robust performance estimation when dealing with imbalanced datasets?

A. Grid search for hyperparameter tuning.
B. Single hold-out validation with a fixed test set.
C. Stratified k-fold cross-validation.
D. Bootstrapping with random sampling.

Answer: C

Explanation:
Stratified k-fold cross-validation is a robust experimental design technique for evaluating machine learning models, especially on imbalanced datasets. It divides the dataset into k folds while preserving the class distribution in each fold, ensuring that the model is evaluated on representative samples of all classes.
NVIDIA's NeMo documentation on model evaluation recommends stratified cross-validation for tasks like text classification to obtain reliable performance estimates, particularly when classes are unevenly distributed (e.g., in sentiment analysis with few negative samples). Option A (single hold-out) is less robust, as it may not capture class imbalance. Option C (bootstrapping) introduces variability and is less suitable for imbalanced data. Option D (grid search) is for hyperparameter tuning, not performance estimation.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/model_finetuning.html

NEW QUESTION # 25
What is the main difference between forward diffusion and reverse diffusion in diffusion models of Generative AI?

A. Forward diffusion uses feed-forward networks, while reverse diffusion uses recurrent networks.
B. Forward diffusion uses bottom-up processing, while reverse diffusion uses top-down processing to generate samples from noise vectors.
C. Forward diffusion focuses on progressively injecting noise into data, while reverse diffusion focuses on generating new samples from the given noise vectors.
D. Forward diffusion focuses on generating a sample from a given noise vector, while reverse diffusion reverses the process by estimating the latent space representation of a given sample.

Answer: C

Explanation:
Diffusion models, a class of generative AI models, operate in two phases: forward diffusion and reverse diffusion. According to NVIDIA's documentation on generative AI (e.g., in the context of NVIDIA's work on generative models), forward diffusion progressively injects noise into a data sample (e.g., an image or text embedding) over multiple steps, transforming it into a noise distribution. Reverse diffusion, conversely, starts with a noise vector and iteratively denoises it to generate a new sample that resembles the training data distribution. This process is central tomodels like DDPM (Denoising Diffusion Probabilistic Models). Option A is incorrect, as forward diffusion adds noise, not generates samples. Option B is false, as diffusion models typically use convolutional or transformer-based architectures, not recurrent networks. Option C is misleading, as diffusion does not align with bottom-up/top-down processing paradigms.
References:
NVIDIA Generative AI Documentation: https://www.nvidia.com/en-us/ai-data-science/generative-ai/ Ho, J., et al. (2020). "Denoising Diffusion Probabilistic Models."

NEW QUESTION # 26
Which of the following claims is correct about quantization in the context of Deep Learning? (Pick the 2 correct responses)

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

Answer: B,C

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 # 27
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