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NCP-AII Practice Test Pdf, Real NCP-AII Exams
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NVIDIA AI Infrastructure Sample Questions (Q286-Q291):
NEW QUESTION # 286
Which of the following statements are true regarding the use of Congestion Management (CM) and Congestion Avoidance (CA) techniques within an InfiniBand fabric using NVIDIA technology? (Select TWO)
- A. CM/CA are not relevant in InfiniBand fabrics because InfiniBand's lossless nature guarantees that no packets will ever be dropped due to congestion.
- B. InfiniBand's Explicit Congestion Notification (ECN) is a CA mechanism that allows switches to signal congestion to endpoints before packet loss occurs.
- C. CM aims to reduce the severity of congestion once it has already occurred, while CA aims to prevent congestion from happening in the first place.
- D. CM can include techniques like rate limiting to throttle traffic flows when congestion is detected.
- E. CM/CA mechanisms are primarily implemented at the IP layer and are independent of the InfiniBand transport layer.
Answer: B,C,D
Explanation:
CM and CA are crucial for maintaining performance in InfiniBand fabrics. CM addresses congestion that has already occurred, while CA tries to prevent it. ECN is a key CA mechanism in InfiniBand. InfiniBand is lossless but experiences performance degradation if congestion occurs; CM helps mitigate this. They are not implemented at the IP layer; they are integral parts of the InfiniBand transport. Rate limiting is a viable CM strategy.
NEW QUESTION # 287
You're deploying BlueField OS to multiple SmartNICs with varying hardware revisions. How can you ensure that the correct device tree is loaded for each specific SmartNIC during the boot process?
- A. Embed the device tree within the kernel image. The kernel will automatically select the correct one during boot.
- B. Relocate the device tree after the OS is running, but before services are started using a custom script.
- C. Manually specify the device tree file in the bootloader configuration for each SmartNI
- D. Create a single device tree that is compatible with all SmartNIC revisions. The kernel will automatically handle compatibility.
- E. Use a bootloader (e.g., U-Boot) that can detect the hardware revision and load the appropriate device tree based on a predefined mapping.
Answer: C
Explanation:
A bootloader like U-Boot is designed to handle hardware detection and conditional loading of resources like device trees. It can identify the SmartNIC revision and load the corresponding DTB file. Creating a single compatible DTB is difficult and may not fully utilize hardware capabilities. Manually specifying the DTB for each NIC is not scalable. Embedding the DTB in the kernel is uncommon. Attempting to modify the device tree at runtime could lead to instability.
NEW QUESTION # 288
After replacing a GPU in a multi-GPU server, you notice that the new GPU is consistently running at a lower clock speed than the other GPUs, even under load. *nvidia-smi' shows the 'Pwr' state as 'P8' for the new GPU, while the others are at 'PO'. What is the MOST probable cause?
- A. The new GPU is overheating and throttling performance.
- B. The driver is not properly recognizing the new GPU's capabilities; reinstall the driver.
- C. The new GPU is a lower-performance model than the other GPUs.
- D. The new GPU is not receiving sufficient power; check the power connections and PSU capacity.
- E. The new GPU requires a firmware update that hasn't been applied.
Answer: D
Explanation:
A GPU stuck in the 'P8' power state indicates that it's not drawing the power it needs to operate at full performance. Insufficient power delivery is the most likely cause. While the new GPU could potentially be overheating or requiring a firmware update, checking power connections and PSU capacity is the first step. Comparing the new GPU's model with the others is also useful, but 'P8' state strongly suggests a power issue. Driver issues are less likely to cause a specific 'P8' state; they typically result in more general performance problems.
NEW QUESTION # 289
You are configuring an NVIDIAAIOO GPU in a server, and after installation and driver setup, lower than the GPU's specified TDP. What are the possible reasons for this? nvidia-smi reports a power limit much
- A. The driver is not correctly installed.
- B. The power supply is not providing enough power.
- C. The GPIJ is in a low-power mode due to inactivity.
- D. The GPU is faulty.
- E. The system BIOS is limiting the power to the PCIe slot.
Answer: E
Explanation:
While the other options are possible, a BIOS setting restricting power to the PCIe slot is a common cause of unexpectedly low power limits reported by 'nvidia-smi'. Always check BIOS settings when troubleshooting power-related issues. The GPU should ramp up power if a workload is presented, if its in low power mode.
NEW QUESTION # 290
You're troubleshooting a DGX-I server exhibiting performance degradation during a large-scale distributed training job. 'nvidia-smu shows all GPUs are detected, but one GPU consistently reports significantly lower utilization than the others. Attempts to reschedule orkloads to that GPU frequently result in CUDA errors. Which of the following is the MOST likely cause and the BEST initial roubleshooting step?
- A. A software bug in the training script utilizing that specific GPU's resources inefficiently; debug the training script.
- B. Insufficient cooling in the server rack; verify adequate airflow and cooling capacity for the rack.
- C. A hardware fault with the GPU, potentially thermal throttling or memory issues; run 'nvidia-smi -i -q' to check temperatures, power limits, and error counts.
- D. A driver issue affecting only one GPU; reinstall NVIDIA drivers completely.
- E. Power supply unit (PSU) overload, causing reduced power delivery to that GPU; monitor PSU load and check PSU specifications.
Answer: C
Explanation:
While all options are possibilities, the consistently lower utilization and CUDA errors point strongly to a hardware fault. Running nvidia-smi -i -q' provides detailed telemetry data, including temperature, power limits, and ECC error counts, which are crucial for diagnosing GPU hardware issues.
NEW QUESTION # 291
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