NVIDIA Certified Associate: Generative AI LLMs (NCA-GENL) Practice Questions: Performance Evaluation and Metrics Domain

Correct Answer: B

Explanation: Implementing content filtering and guardrails is crucial for ensuring responsible AI deployment, as it helps prevent the generation of harmful or biased content. Using larger models, relying only on pre-trained models, or increasing training epochs do not inherently contribute to responsible AI practices.

Correct Answer: B

Explanation: NVIDIA NeMo offers pre-built pipelines for tasks like text classification and question answering, which simplifies the fine-tuning process and reduces development time. Option A is incorrect because NeMo does not automatically reduce model size; model optimization requires specific techniques. Option C, while true, is not the primary advantage in the context of fine-tuning. Option D is incorrect because fine-tuning typically requires labeled data.

Correct Answer: B

Explanation: LoRA reduces computational costs by allowing fine-tuning of only a subset of parameters, which is efficient for adapting large models to specific tasks without retraining the entire model. Option A is incorrect as LoRA actually reduces the number of parameters that need to be updated. Option C is incorrect because LoRA doesn't affect data preprocessing. Option D is incorrect as LoRA is not related to security or encryption.

Correct Answer: B

Explanation: Mixed precision training, combined with gradient accumulation, allows for efficient handling of large datasets by reducing memory usage and speeding up computations without sacrificing model accuracy. NVIDIA NeMo supports mixed precision training, which utilizes FP16 precision to decrease memory load. Option A would increase memory usage, Option C could degrade model performance, and Option D would be inefficient for training large models.

Correct Answer: B

Explanation: Mixed precision inference reduces memory usage by using lower precision data types (e.g., FP16) without a significant loss in model accuracy, thus maintaining low latency. Option A might increase memory usage due to larger batch processing. Option C could increase memory usage as more instances require more memory. Option D is incorrect because dynamic batching helps manage memory more efficiently by optimizing the use of available resources.

Correct Answer: B

Explanation: Multi-node orchestration is critical for managing large-scale deployments across multiple nodes, as it allows for efficient resource management and scaling of AI workloads. The NGC catalog provides access to pre-trained models, which is beneficial but not specific to multi-node deployments. Single-node optimization is not applicable when scaling across multiple nodes.

Correct Answer: B

Explanation: Implementing bias detection algorithms is an effective way to identify and mitigate biases in language models. NVIDIA NeMo provides tools and frameworks to evaluate and reduce bias, ensuring the models are fair and ethical. Increasing data size or reducing parameters does not inherently address bias, while supervised fine-tuning alone may not mitigate existing biases.

Correct Answer: B

Explanation: Mixed precision training, particularly using FP16, is a common strategy to reduce memory usage while maintaining performance. TensorRT-LLM supports mixed precision, which allows for reduced memory footprint and faster computation. Full precision (FP32) increases memory usage, larger batch sizes can lead to out-of-memory errors, and a larger context window increases memory requirements.

Correct Answer: B

Explanation: Multimodal applications require balancing computational resources between different data modalities (e.g., text, images) to optimize performance on NVIDIA DGX systems. Option A is incorrect as multimodal applications involve various data types. Option C is incorrect as computational balance involves more than just memory. Option D is incorrect as different modalities may require distinct processing pipelines.

Correct Answer: B

Explanation: Quantization reduces the memory footprint of a model by converting its weights and activations from floating-point precision to lower precision (e.g., INT8), which also leads to faster inference times. It does not inherently increase model accuracy, speed up training, or improve interpretability.