NVIDIA Certified Associate: Generative AI LLMs (NCA-GENL) Practice Questions: NVIDIA AI Enterprise Platform Domain

Correct Answer: B

Explanation: Mixed Precision Training leverages the use of FP16 and FP32 data types to reduce memory consumption and increase training speed without significantly compromising model accuracy. Gradient Accumulation helps with smaller batch sizes but doesn't reduce memory usage per se. Data Augmentation is more relevant to data diversity, and Batch Normalization is not typically used in transformer architectures.

Correct Answer: B

Explanation: LoRA (Low-Rank Adaptation) is a technique that allows fine-tuning of large models by updating only a small subset of parameters, which reduces computational cost and memory usage. Full model fine-tuning updates all parameters, mixed precision training reduces memory footprint but does not focus on parameter updates, and gradient clipping is used to prevent exploding gradients.

Correct Answer: B

Explanation: Mixed precision training uses both 16-bit and 32-bit floating point numbers to accelerate the training process and reduce memory usage without significantly impacting model accuracy. This approach leverages NVIDIA's Tensor Cores, which are optimized for mixed precision operations, thus reducing training time and resource consumption. While it does not inherently increase accuracy, simplify architecture, or enhance interpretability, it allows for training larger models more efficiently.

Correct Answer: C

Explanation: Chain-of-thought prompting is used to guide generative AI models in generating more coherent and logically consistent responses by structuring prompts in a way that mimics human reasoning. This technique helps the model to follow a logical sequence of thoughts, improving the quality of the output. It does not directly affect training efficiency, interpretability, or inference latency.

Correct Answer: B

Explanation: RAG combines retrieval of relevant information from external data sources with generation capabilities, which enhances the factual accuracy of the generated content by grounding it in real-world data. It does not reduce model size, increase training speed, or simplify deployment directly, but rather focuses on improving the quality of outputs.

Correct Answer: C

Explanation: Memory pooling is an optimization technique that helps manage memory usage by reusing memory blocks, thus reducing the overhead of frequent memory allocation and deallocation. Layer fusion and kernel auto-tuning focus on computational efficiency, while precision calibration adjusts numerical precision to balance performance and accuracy.

Correct Answer: B

Explanation: LoRA (Low-Rank Adaptation) is a technique used to fine-tune large language models efficiently by updating only a small number of parameters, which reduces the computational cost and memory requirements. This makes it suitable for environments with limited resources. Full model fine-tuning requires more resources, zero-shot learning doesn't involve fine-tuning, and prompt engineering is more about designing effective inputs rather than model parameter updates.

Correct Answer: C

Explanation: NVIDIA Triton Inference Server provides support for multiple frameworks and backends, automatic model versioning, and scalable and flexible deployment. However, it does not include built-in prompt engineering tools, which are typically handled at the application level or using other specialized libraries.

Correct Answer: B

Explanation: Mixed precision training uses both 16-bit and 32-bit floating point types, which reduces memory usage and increases computational efficiency, leading to faster training times. It does not inherently increase model accuracy, simplify architecture, or improve interpretability of outputs, though it allows for larger models to be trained within the same resource constraints.

Correct Answer: B

Explanation: The NGC catalog is a comprehensive repository that offers pre-trained models, AI software containers, and other resources essential for deploying generative AI applications. It simplifies access to state-of-the-art models and tools, accelerating the development and deployment process. It does not function as a marketplace for hardware, a data collection service, or a cloud resource manager.