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NVIDIA AI Infrastructure Sample Questions (Q292-Q297):
NEW QUESTION # 292
You encounter an error during the BlueField OS flashing process using 'bfboot: 'ERROR: Could not detect a BlueField device'. Which of the following steps is MOST likely to resolve the issue?
Answer: E
Explanation:
The most basic and common cause of this error is a physical connection issue. Ensuring the device is powered and connected is the first step. While other options might be relevant in specific scenarios, connectivity is the most probable cause.
NEW QUESTION # 293
You are implementing a security policy on a BlueField-2 DPU to filter traffic based on specific application signatures. Which technology, supported by BlueField, allows you to achieve deep packet inspection (DPI) and apply security rules based on the detected application?
Answer: B
Explanation:
eBPF with XDP is the most suitable technology for deep packet inspection (DPI) on BlueField. It allows you to run custom code at near-line speed to inspect packets and apply security rules based on application signatures. TC and Netfilter are less efficient for DPI, OVS/OpenFlow are more for switching policies, and IPsec focuses on encryption.
NEW QUESTION # 294
An AI inference workload running on a server equipped with NVIDIA TensorRT is experiencing intermittent performance degradation. Profiling reveals potential issues with GPU utilization. Which command-line tool is best suited to provide real-time GPU performance metrics, including utilization, memory usage, and power consumption, during workload execution?
Answer: C
Explanation:
'nvidia-smi' (NVIDIA System Management Interface) is the primary tool for monitoring GPU performance in real-time, including utilization, memory usage, and power consumption. While 'nvprof is a profiler, it's more suited for in-depth analysis after the run. 'nvitop' and gpustat are third-party tools that provide similar functionalities as -nvidia-smr. 'tegrastats' is specific to NVIDIA Jetson devices.
NEW QUESTION # 295
Which of the following techniques can be used to optimize storage performance for deep learning training?
Answer: A,D,E
Explanation:
Data prefetching anticipates future data needs and loads data into the cache before it is requested. A larger block size can improve I/O throughput for large files. Data sharding distributes data across multiple storage devices to increase parallelism. Data compression, while saving space, can add overhead during training. Data deduplication is not normally usefull for training data sets.
NEW QUESTION # 296
You need to verify the NVLink connectivity between GPUs in a DGX server. Which command-line utility is the MOST reliable and provides detailed NVLink status?
Answer: D
Explanation:
'dcgmi diag -t 1004' is the correct command. 'nvidia-smi' provides basic GPIJ information, but 'dcgmi diag -t 1004' (part of the Data Center GPU Manager) provides specific diagnostic tests for NVLink connectivity. 'Ispci' lists PCle devices, not specifically NVLink. 'gpustat' is a monitoring tool. 'nvlink_info' is hypothetical.
NEW QUESTION # 297
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