Free NCA-GENL sample questions

Correct answer: NVIDIA RAPIDS cuDF

NVIDIA RAPIDS cuDF is the correct choice. It provides a pandas-like API for data manipulation that runs on GPUs, making it ideal for accelerating data preprocessing tasks like cleaning and tokenization on large datasets. Triton is for inference serving, TensorRT-LLM is for optimizing inference, and NeMo is a framework for building and training models.

Correct answer: Augmenting the original user prompt with the retrieved context.

In a standard RAG pipeline, after the system retrieves the most relevant document chunks (context) from the vector database based on the user's query, the next step is to combine this retrieved context with the original user prompt. This new, augmented prompt is then sent to the LLM to generate a contextually-aware answer. Generating the final answer happens after the prompt is augmented.

Correct answer: In-flight Batching

In-flight batching (also known as continuous batching) is the correct feature. Unlike dynamic batching, which waits to form a complete batch before processing, in-flight batching can add new requests to a batch that is already being processed. This allows shorter requests to be processed and return while longer ones are still running, effectively eliminating head-of-line blocking and improving overall throughput and latency.

Correct answers: NVIDIA NeMo Guardrails; Retrieval-Augmented Generation (RAG) on internal patient data

NeMo Guardrails is specifically designed to control the topics an LLM can discuss, prevent it from executing unsafe actions, and ensure it stays within predefined conversational boundaries. This directly addresses the need to prevent off-topic conversations.

Using a RAG architecture that only pulls from the hospital's internal, secure patient database ensures that the LLM's responses are grounded in factual, relevant data and do not hallucinate or access external information. This enhances both accuracy and privacy.

Correct answer: False

The statement is false. LoRA is a Parameter-Efficient Fine-Tuning (PEFT) method. It works by freezing the original pre-trained model weights and injecting small, trainable rank-decomposition matrices (adapters) into the layers of the Transformer architecture. Only these new, much smaller matrices are updated during training, which drastically reduces the number of trainable parameters and computational cost.

Correct answer: Pipeline Parallelism

Pipeline Parallelism is the technique of splitting the layers of a neural network model across multiple GPUs. Each GPU processes a subset of the model's layers (a stage) in a pipeline fashion. This is used when a model is too large to fit into a single GPU's memory. Data Parallelism replicates the model on each GPU, and Tensor Parallelism splits individual layers/tensors across GPUs.

Correct answer: ROUGE

ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a set of metrics used for evaluating automatic summarization and machine translation. It works by comparing an automatically produced summary against a set of reference summaries (typically human-written) and measures the overlap of n-grams, word sequences, and word pairs.

Correct answer: NeMo Guardrails

NVIDIA NeMo Guardrails is an open-source toolkit for adding programmable, rule-based controls to LLM-based applications. It allows developers to define specific conversational paths, control topics, prevent the model from accessing certain tools, and filter out undesirable content, making it the correct choice for ensuring safe and on-topic conversations.

Correct answer: To weigh the importance of different words in the input sequence relative to each other.

The self-attention mechanism allows the model to associate each word in the input sequence with other words in the same sequence. It calculates attention scores that determine how much focus to place on other words when encoding a specific word. This allows the model to capture long-range dependencies and understand context within the sequence, which is a key advantage over recurrent architectures.

Correct answer: Output Regeneration/Plagiarism

This scenario describes output regeneration, also known as plagiarism or regurgitation. It occurs when a generative model reproduces verbatim or near-verbatim excerpts from its training data. If the training data includes copyrighted material, this can lead to copyright infringement. This is a key concern in responsible AI development.