NCP-GENL Practice Questions: Model Optimization Domain

Correct Answer: B

Explanation: Enabling dynamic batching in Triton allows the server to automatically adjust batch sizes based on the incoming request patterns, optimizing GPU utilization and improving throughput. Static batching (A) with a fixed size may not adapt well to fluctuating workloads. Deploying multiple instances (C) with manual load balancing increases complexity and does not inherently optimize resource use. Running the model on CPU (D) would likely degrade performance, as GPUs are specifically optimized for such tasks. Dynamic batching is a best practice for handling variable workloads efficiently in NVIDIA's ecosystem.

Correct Answer: C

Explanation: Using TensorRT-LLM is a highly effective method for optimizing NVIDIA models for inference, particularly on GPU architectures like DGX systems. It reduces latency by optimizing the model's execution, making it faster without compromising accuracy. Option A, pruning, can help reduce model size but might affect accuracy if not done carefully. Option B, quantization-aware training, is useful but primarily reduces model size, not necessarily latency. Option D, RLHF, focuses on improving model behavior and alignment with human expectations, not directly on latency reduction.

Correct Answer: A

Explanation: Quantization to INT8 can lead to accuracy loss if not calibrated properly. Using calibration data helps adjust the model's weights and activations for better accuracy. Option B involves retraining, which is not directly related to inference optimization. Option C might improve precision but at the cost of performance. Option D is less common and typically does not cause significant accuracy drops.

Correct Answer: B

Explanation: LoRA (Low-Rank Adaptation) is designed to reduce the number of parameters that need to be updated during fine-tuning, which saves memory and computational resources. This is particularly beneficial for large language models where full fine-tuning is computationally expensive. Option A is incorrect as LoRA does not directly affect interpretability. Option C is incorrect because LoRA does not introduce redundancy but rather reduces parameter count. Option D is incorrect as LoRA does not enable real-time adaptation without retraining.

Correct Answer: D

Explanation: The correct answer is D. Deploying multiple model replicas with load balancing ensures high availability and reliability by distributing the load and providing redundancy. Option A focuses on efficiency and may not directly address reliability. Option B can help with recovery but doesn't ensure real-time reliability. Option C optimizes data handling but doesn't directly impact model reliability. Best-practice takeaway: Load balancing with multiple replicas is a common strategy to enhance the reliability of production systems.

Correct Answer: C

Explanation: Option C is correct because applying post-training quantization using TensorRT can significantly reduce model size and improve inference speed by converting weights to a lower precision format. Option A, while potentially beneficial, primarily reduces memory usage rather than latency. Option B is useful for data preprocessing but does not directly impact model inference latency. Option D would likely increase computational demand, thereby increasing latency. Best practice involves using quantization to optimize inference speed.

Correct Answer: B

Explanation: Option B is correct because uneven GPU utilization often results from imbalanced data distribution across nodes. Ensuring balanced data sharding can improve utilization. Option A relates to training stability rather than utilization. Option C is unlikely as most optimizers are compatible with distributed training. Option D might help but does not address the root cause of uneven distribution. Best practice involves checking data distribution as a first step in troubleshooting uneven GPU utilization.

Correct Answer: A

Explanation: Uneven GPU utilization often results from incorrect model partitioning. Using model parallelism in NeMo can distribute the model more evenly across GPUs, improving utilization and reducing latency. Option B addresses training data rather than model deployment. Option C is more relevant to training efficiency rather than inference performance. Option D would not leverage the full potential of a distributed setup.

Correct Answer: A

Explanation: Option A is correct because mixed precision training with NVIDIA Apex utilizes Tensor Cores on NVIDIA GPUs, which can significantly speed up training by allowing operations to be performed at lower precision while maintaining model accuracy. Option B, increasing the learning rate, could lead to instability and poor convergence. Option C, using less data, might speed up training but would likely degrade model performance. Option D, switching optimizers, may not have a significant impact on training speed compared to mixed precision. Best practice is to use mixed precision training to optimize performance on NVIDIA hardware.

Correct Answer: B

Explanation: Reinforcement Learning from Human Feedback (RLHF) is an effective method to align model outputs with human values and reduce biases by fine-tuning the model based on feedback. Option A, differential privacy, focuses on data privacy rather than bias mitigation. Option C, LoRA, is more about parameter-efficient fine-tuning and does not specifically target bias. Option D, quantization, is primarily used for model size reduction and efficiency, not bias correction.