NVIDIA NCA-GENL Generative AI LLMs Exam Practice Test

Answer : A

The transformer architecture, introduced in 'Attention is All You Need' (Vaswani et al., 2017), is particularly effective for language modeling due to its ability to handle long sequences. Unlike RNNs, which struggle with long-term dependencies due to sequential processing, transformers use self-attention mechanisms to process all tokens in a sequence simultaneously, capturing relationships across long distances. NVIDIA's NeMo documentation emphasizes that transformers excel in tasks like language modeling because their attention mechanisms scale well with sequence length, especially with optimizations like sparse attention or efficient attention variants. Option B (embeddings) is a component, not a unique strength. Option C (class tokens) is specific to certain models like BERT, not a general transformer feature. Option D (translations) is an application, not a structural advantage.

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

Answer : B

The paired t-test is the most appropriate statistical test to compare the performance (e.g., accuracy) of two large language models (LLMs) on the same question-answering dataset, assuming the data follows a normal distribution. This test evaluates whether the mean difference in paired observations (e.g., accuracy on each question) is statistically significant. NVIDIA's documentation on model evaluation in NeMo suggests using paired statistical tests for comparing model performance on identical datasets to account for correlated errors. Option A (Chi-squared test) is for categorical data, not continuous metrics like accuracy. Option C (Mann-Whitney U test) is non-parametric and used for non-normal data. Option D (ANOVA) is for comparing more than two groups, not two models.

NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/model_finetuning.html

Answer : B

NVIDIA Triton Inference Server is designed for high-performance model deployment, and dynamic batching is a key optimization technique for reducing latency while maintaining high throughput in real-time applications like chatbots. Dynamic batching groups multiple inference requests into a single batch, leveraging GPU parallelism to process them simultaneously, thus reducing per-request latency. According to NVIDIA's Triton documentation, this is particularly effective for LLMs with variable input sizes, as it maximizes resource utilization. Option A is incorrect, as increasing parameters increases latency. Option C may reduce latency but sacrifices context and quality. Option D is false, as CPU-based inference is slower than GPU-based for LLMs.

NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html

Answer : C

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.

NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

Answer : C

LangChain is a framework designed to simplify the development of applications powered by large language models (LLMs) by orchestrating various components, such as LLMs, external data sources, memory, and tools, into cohesive workflows. According to NVIDIA's documentation on generative AI workflows, particularly in the context of integrating LLMs with external systems, LangChain enables developers to build complex applications by chaining together prompts, retrieval systems (e.g., for RAG), and memory modules to maintain context across interactions. For example, LangChain can integrate an LLM with a vector database for retrieval-augmented generation or manage conversational history for chatbots. Option A is incorrect, as LangChain complements, not replaces, programming languages. Option B is wrong, as LangChain does not modify model size. Option D is inaccurate, as hardware management is handled by platforms like NVIDIA Triton, not LangChain.

NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

LangChain Official Documentation: https://python.langchain.com/docs/get_started/introduction

Answer : D

Chunking in Retrieval-Augmented Generation (RAG) refers to the process of splitting large text documents into smaller, meaningful segments (or chunks) to facilitate efficient retrieval and processing by the LLM. According to NVIDIA's documentation on RAG workflows (e.g., in NeMo and Triton), chunking ensures that retrieved text fits within the model's context window and is relevant to the query, improving the quality of generated responses. For example, a long document might be divided into paragraphs or sentences to allow the retrieval component to select only the most pertinent chunks. Option A is incorrect because chunking does not involve rewriting text. Option B is wrong, as chunking is not about generating random text. Option C is unrelated, as chunking is not a training process.

NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

Lewis, P., et al. (2020). 'Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.'

Answer : A

A/B testing is a controlled experimentation method used to compare two versions of a system (e.g., two model variants) to determine which performs better based on a predefined metric (e.g., user engagement, accuracy). NVIDIA's documentation on model optimization and deployment, such as with Triton Inference Server, highlights A/B testing as a method to validate model improvements in real-world settings by comparing performance metrics statistically. For a recommendation system, A/B testing might compare click-through rates between two models. Option B is incorrect, as A/B testing focuses on outcomes, not designer commentary. Option C is misleading, as robustness is tested via other methods (e.g., stress testing). Option D is partially true but narrow, as A/B testing evaluates broader performance metrics, not just latency.

NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html