NCP-AIO Practice Questions: Troubleshooting and Optimization Domain

Correct Answer: A

Explanation: Before making any changes to the system, it's important to gather data to understand the root cause of the performance degradation. NVIDIA's Data Center GPU Manager (DCGM) provides insights into GPU utilization metrics, which can help identify if the issue is related to GPU resource constraints. Options B, C, and D involve making changes that may not address the root cause and could lead to unnecessary disruptions.

Correct Answer: C

Explanation: Analyzing resource allocation in the Kubernetes dashboard can help determine if the model is receiving the necessary resources (e.g., GPU, memory) for optimal performance. Verbose logging and TensorRT optimization are useful but should be considered after verifying resource allocation.

Correct Answer: B

Explanation: Analyzing the Triton server logs can provide insights into bottlenecks and latency issues. Increasing concurrent model instances without understanding the cause may exacerbate the problem. Switching model formats might not address the root cause of latency. Enabling batch inferencing can improve throughput but not necessarily diagnose latency issues.

Correct Answer: A

Explanation: NVIDIA TensorRT is a high-performance deep learning inference optimizer and runtime that can be used to optimize neural network models for production deployment, reducing inference latency. NVIDIA DIGITS is a training platform, DeepStream is for video analytics, and CUDA Toolkit provides a development environment for building GPU-accelerated applications, not specifically for inference optimization.

Correct Answer: A

Explanation: Implementing detailed logging (A) helps capture performance metrics and identify patterns or bottlenecks causing slowdowns. Upgrading the network (B) or deploying additional nodes (D) might help if the cause is known to be network or resource-related, but logging provides the necessary insights. Scheduling maintenance during peak times (C) is counterproductive and doesn't address the issue.

Correct Answer: A

Explanation: A 'CUDA initialization error' typically indicates that the container does not have the necessary CUDA libraries installed. Kubernetes configuration and GPU mode are less likely to cause this error, and while version compatibility is important, it usually results in different error messages.

Correct Answer: B

Explanation: Checking the application logs for error messages related to GPU usage is the first step in diagnosing the issue. These logs can provide insights into why the workloads are not using the GPUs effectively. Reinstalling drivers or upgrading Kubernetes without identifying the problem might not address the root cause.

Correct Answer: B

Explanation: Using network monitoring tools to trace packet loss and latency can help diagnose connectivity issues by providing insights into network performance. Implementing retry logic (A) or increasing timeouts (C) are workarounds, and checking firewall settings (D) is only relevant if ports are blocked.

Correct Answer: D

Explanation: Utilizing NVIDIA's MIG (Multi-Instance GPU) capability allows partitioning of a GPU into multiple instances, which can help manage memory usage more effectively. GPU memory oversubscription is not a viable option. Model parallelism might not address memory issues directly. Increasing swap space affects CPU memory, not GPU memory.

Correct Answer: B

Explanation: Analyzing and adjusting the model's batch size configuration is the most effective first step because batch size directly impacts throughput and latency. A suboptimal batch size can lead to inefficient GPU utilization and increased latency. The other options, while potentially useful in other scenarios, do not directly address the initial step of diagnosing model configuration issues.