NCA-GENL Practice Questions: Training and Fine-tuning Techniques Domain

Correct Answer: B

Explanation: Implementing vector databases for embedding retrieval is crucial for RAG as it allows the model to access and incorporate relevant external information, enhancing context understanding and response generation. Using only pre-trained models (A) limits context, increasing token limit (C) does not directly integrate external data, and focusing solely on supervised fine-tuning (D) does not leverage retrieval mechanisms.

Correct Answer: B

Explanation: LoRA (Low-Rank Adaptation) is a technique used in NVIDIA NeMo that allows fine-tuning of a pre-trained model by updating a small subset of parameters, thus requiring less computational resources and maintaining most of the model's original parameters frozen.

Correct Answer: A

Explanation: LoRA (Low-Rank Adaptation) is a technique used to efficiently fine-tune large language models by injecting trainable low-rank matrices into the model weights, reducing the computational cost. Gradient Accumulation and Mixed Precision Training are techniques to manage memory and precision, respectively, and RLHF (Reinforcement Learning with Human Feedback) is a separate fine-tuning strategy not specifically aimed at resource efficiency.

Correct Answer: B

Explanation: Utilizing NVIDIA's Multi-Instance GPU (MIG) technology allows for optimal memory management by partitioning GPU resources, enabling multiple isolated instances to run efficiently. Option A is incorrect as running multiple instances without limits can lead to memory exhaustion. Option C is incorrect because memory sharing can be beneficial in certain contexts. Option D is incorrect as allocating all memory to a single instance can prevent efficient resource utilization.

Correct Answer: B

Explanation: Dropout is a regularization technique that helps prevent overfitting by randomly dropping units from the neural network during training. NVIDIA NeMo supports this technique to ensure robust model generalization.

Correct Answer: B

Explanation: ROUGE is the most suitable evaluation metric for assessing the fluency and coherence of a language model's output in a summarization task. It measures the overlap of n-grams between the generated summary and a reference summary, capturing fluency and relevance. BLEU (A) is more suited for translation tasks. Perplexity (C) measures how well a model predicts a sample and is not directly indicative of fluency. A/B testing (D) is a broader evaluation method that involves user feedback rather than direct metric measurement.

Correct Answer: B

Explanation: Temperature sampling is a technique used to control the randomness of predictions in generative models. By adjusting the temperature parameter, you can make the model outputs more diverse (higher temperature) or more deterministic (lower temperature). Beam search (A) and greedy decoding (C) focus on finding the most likely sequence, which might reduce diversity. Top-k sampling (D) can also increase diversity but is less flexible than temperature sampling.

Correct Answer: A

Explanation: Gradient checkpointing is a technique that reduces memory usage by storing only a subset of activations during the forward pass and recomputing them during the backward pass. This is particularly useful in the context of large models like those fine-tuned using NVIDIA's NeMo framework. Full precision training (B) increases memory usage, batch normalization (C) is not directly related to memory reduction, and data parallelism (D) focuses on distributing the workload across multiple devices rather than reducing memory usage.

Correct Answer: B

Explanation: Instruction tuning involves providing explicit instructions that guide the model's outputs, which can enhance performance by aligning the model's responses more closely with user expectations. Option A is incorrect because instruction tuning does not affect vocabulary size. Option C is incorrect as it does not reduce computational complexity. Option D is incorrect because instruction tuning complements rather than replaces fine-tuning.

Correct Answer: B

Explanation: A common challenge in deploying LLMs is managing high inference latency. NVIDIA Triton addresses this by supporting model optimization techniques such as TensorRT and efficient batching strategies, which reduce latency. Option A is incorrect as Triton does not primarily focus on interpretability. Option C is incorrect because Triton does not perform automatic translation. Option D is incorrect as Triton is focused on inference, not training.