NCA-GENL Practice Questions: Prompt Engineering and Optimization Domain

Correct Answer: B

Explanation: Implementing strict input validation helps prevent prompt injection attacks by ensuring that inputs are sanitized and conform to expected formats. Using complex language models or increasing model size does not directly address injection vulnerabilities, and deploying on secure hardware does not mitigate software-level attacks.

Correct Answer: B

Explanation: Dynamic batching in NVIDIA Triton allows the server to automatically batch incoming requests, optimizing GPU utilization and maintaining low latency under varying loads. Asynchronous execution is a general optimization technique, model ensembling combines predictions from multiple models, and data caching focuses on data access speed, not directly on load handling.

Correct Answer: B

Explanation: Chain-of-Thought prompting helps models perform better on complex reasoning tasks by structuring the input to mimic step-by-step logical reasoning. This improves interpretability but does not directly affect training speed, memory usage, or tokenization.

Correct Answer: D

Explanation: NVIDIA TensorRT-LLM is specifically designed to manage memory effectively during the deployment of large-scale generative AI models by optimizing the model and execution plan for NVIDIA GPUs. Triton Inference Server facilitates deployment but does not specifically optimize memory. RAPIDS and Jetson are not focused on memory management for LLMs.

Correct Answer: B

Explanation: TensorRT-LLM is specifically designed to optimize inference performance for large language models by providing high-performance, low-latency execution. It integrates with Triton Inference Server to enhance deployment efficiency. NeMo is more focused on model development, while CUDA Toolkit and DeepStream SDK are not specifically tailored for LLM optimization.

Correct Answer: B

Explanation: Chain-of-thought prompting is used to enhance a model's reasoning ability by breaking down complex tasks into a series of logical steps or thoughts. This approach helps the model generate more coherent and logical responses. Options A, C, and D do not relate to the purpose of chain-of-thought prompting.

Correct Answer: C

Explanation: ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is specifically designed to evaluate the quality of summaries by comparing overlap between the generated and reference summaries. BLEU is more suited for translation tasks, perplexity measures model uncertainty, and F1 Score is a general metric for classification tasks.

Correct Answer: B

Explanation: Chain-of-thought prompting involves guiding the model through a detailed reasoning process, which helps improve the accuracy of responses, especially for complex queries. Few-shot learning provides limited examples, instruction tuning involves refining task-specific instructions, and prompt injection prevention focuses on security aspects.

Correct Answer: B

Explanation: Chain-of-thought prompting helps LLMs generate intermediate reasoning steps, which can improve their problem-solving abilities and decision-making processes. This approach does not necessarily reduce token usage or increase training speed, and it does not guarantee deterministic outputs, as LLMs are inherently probabilistic.

Correct Answer: B

Explanation: NVIDIA NeMo is designed to facilitate the fine-tuning and deployment of large language models. One of its primary advantages is the seamless integration with NVIDIA Triton Inference Server, which allows for optimized inference through features like parallel execution and dynamic batching. This integration is crucial for deploying LLMs efficiently in production environments. Options A and C are incorrect as NeMo requires customization and does not focus on dataset generation. Option D is incorrect because NeMo uses standard attention mechanisms.