NCA-GENL Practice Questions: RAG (Retrieval-Augmented Generation) Domain

Correct Answer: B

Explanation: LoRA is a technique that reduces the number of trainable parameters during fine-tuning by using low-rank matrices, which significantly decreases the computational resources required. This makes it feasible to fine-tune large models on limited hardware. While it does not directly reduce model size or improve accuracy without training, it is a resource-efficient method for fine-tuning. It does not relate to vector databases.

Correct Answer: B

Explanation: ROUGE is the most suitable metric for evaluating document summarization tasks as it measures the overlap of n-grams between the generated summary and reference summaries. BLEU (A) is more commonly used for translation tasks, perplexity (C) measures model uncertainty, and A/B testing (D) is a method for comparing two versions of a system.

Correct Answer: B

Explanation: Mixed precision training is an effective technique to reduce training time by using lower precision (e.g., FP16) for most computations, which speeds up processing and reduces memory usage while maintaining model accuracy. Gradient accumulation (A) helps in simulating larger batch sizes but doesn't directly reduce training time. Layer normalization (C) and tokenization (D) are not techniques aimed at reducing training time.

Correct Answer: B

Explanation: TensorRT-LLM is specifically designed for optimizing large language models for inference by reducing latency and enhancing throughput. It uses techniques like precision calibration and layer fusion to optimize models for deployment. NVIDIA NeMo is primarily for model training and fine-tuning, NVIDIA AI Enterprise provides a comprehensive suite for AI solutions, and NGC Catalog hosts pre-trained models and resources.

Correct Answer: C

Explanation: DGX systems are designed to provide high-performance computing capabilities specifically optimized for AI and deep learning workloads, making them ideal for deploying resource-intensive RAG models. Options A, B, and D do not accurately describe the features of DGX systems.

Correct Answer: B

Explanation: In a RAG system, the embedding model's primary role is to convert text into numerical vectors that capture semantic meaning. This facilitates semantic search within the retrieval component, allowing it to find documents or information that are contextually relevant to the query. Option A is incorrect as it describes tokenization, not embedding. Option C is incorrect as compression is not the primary function of embedding models. Option D is incorrect because summarization is not the typical role of embeddings in retrieval.

Correct Answer: C

Explanation: A key challenge in deploying RAG systems with Triton Inference Server is managing latency while maintaining high throughput, especially when serving multiple models or handling large-scale requests. Triton supports multi-model serving (A) and is designed to integrate with various data sources (B). It is also optimized for NVIDIA hardware (D), making C the correct answer.

Correct Answer: C

Explanation: Chunk optimization involves breaking down the input into smaller, manageable parts, reducing the effective context window size for each inference pass, thereby mitigating latency issues. Decreasing transformer layers affects model capacity, mixed precision is for training efficiency, and larger models would likely increase latency further.

Correct Answer: B

Explanation: NeMo supports model parallelism and mixed precision training, which are best practices for optimizing the performance and efficiency of large language models.

Correct Answer: B

Explanation: LoRA reduces the memory footprint during fine-tuning by introducing low-rank adaptations, allowing large models to be fine-tuned more efficiently without the need for extensive computational resources. Options A, C, and D do not reflect the primary purpose of LoRA.