NVIDIA NCA-GENL Exam Questions

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.

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

Positional encoding is a vital component of the Transformer architecture, as emphasized in NVIDIA's Generative AI and LLMs course. Transformers lack the inherent sequential processing of recurrent neural networks, so they rely on positional encoding to incorporate information about the order of tokens in the input sequence. This is typically achieved by adding fixed or learned vectors (e.g., sine and cosine functions) to the token embeddings, where each position in the sequence has a unique encoding. This allows the model to distinguish the relative or absolute positions of tokens, enabling it to understand word order in tasks like translation or text generation. For example, in the sentence 'The cat sleeps,' positional encoding ensures the model knows 'cat' is the second token and 'sleeps' is the third. Option A is incorrect, as positional encoding does not remove information but adds positional context. Option B is wrong because semantic meaning is captured by token embeddings, not positional encoding. Option D is also inaccurate, as the importance of tokens is determined by the attention mechanism, not positional encoding. The course notes: 'Positional encodings are used in Transformers to provide information about the order of tokens in the input sequence, enabling the model to process sequences effectively.'

Instructed large language models (LLMs), such as those supported by NVIDIA's NeMo framework, have significant advantages over smaller, traditional models:

Option D: LLMs often have cheaper computational costs during inference for certain tasks because they can generalize across multiple tasks without requiring task-specific retraining, unlike smaller models that may need separate models per task.

Option E: A single generic LLM can perform multiple tasks (e.g., text generation, classification, translation) due to its broad pre-training, unlike smaller models that are typically task-specific.

Option A is incorrect, as LLMs require large amounts of data, often labeled or curated, for pre-training. Option B is false, as LLMs typically have higher latency and lower throughput due to their size. Option C is misleading, as LLMs are often less interpretable than smaller models.

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

Brown, T., et al. (2020). 'Language Models are Few-Shot Learners.'

When test data is unavailable, cross-validation is the most effective method to assess an AI model's performance using only the training dataset. Cross-validation involves splitting the training data into multiple subsets (folds), training the model on some folds, and validating it on others, repeating this process to estimate generalization performance. NVIDIA's documentation on machine learning workflows, particularly in the NeMo framework for model evaluation, highlights k-fold cross-validation as a standard technique for robust performance assessment when a separate test set is not available. Option B (randomized controlled trial) is a clinical or experimental method, not typically used for model evaluation. Option C (average entropy approximation) is not a standard evaluation method. Option D (greedy decoding) is a generation strategy for LLMs, not an evaluation technique.

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

Goodfellow, I., et al. (2016). 'Deep Learning.' MIT Press.

Red-teaming exercises involve systematically testing a large language model (LLM) by probing it with adversarial or challenging inputs to uncover vulnerabilities, such as biases, unsafe responses, or harmful outputs. NVIDIA's Trustworthy AI framework emphasizes red-teaming as a critical step in the alignment process to ensure LLMs adhere to ethical standards and societal values. By simulating worst-case scenarios, red-teaming helps developers identify and mitigate risks, such as generating toxic content or reinforcing stereotypes, before deployment. Option A is incorrect, as red-teaming focuses on safety, not speed. Option C is false, as it does not involve model size. Option D is wrong, as red-teaming is about evaluation, not data collection.

NVIDIA Trustworthy AI: https://www.nvidia.com/en-us/ai-data-science/trustworthy-ai/