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| Topic | Details |
|---|
| Topic 1 | - Control Plane Installation and Configuration: Covers deploying the software stack including Base Command Manager, OS, Slurm
- Enroot
- Pyxis, NVIDIA GPU and DOCA drivers, container toolkit, and NGC CLI.
|
| Topic 2 | - Cluster Test and Verification: Covers full cluster validation through HPL and NCCL benchmarks, NVLink and fabric bandwidth tests, cable and firmware checks, and burn-in testing using HPL, NCCL, and NeMo.
|
| Topic 3 | - System and Server Bring-up: Covers end-to-end physical setup of GPU-based AI infrastructure, including BMC
- OOB
- TPM configuration, firmware upgrades, hardware installation, and power and cooling validation to ensure servers are workload-ready.
|
| Topic 4 | - Troubleshoot and Optimize: Covers identifying and replacing faulty hardware components such as GPUs, network cards, and power supplies, along with performance optimization for AMD
- Intel servers and storage.
|
| Topic 5 | - Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
|
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NVIDIA NCP-AII Exam | Reliable NCP-AII Test Vce - Excellent Exam Tool Guaranteed
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NVIDIA AI Infrastructure Sample Questions (Q113-Q118):
NEW QUESTION # 113
A system administrator receives an alert about a potential hardware fault on an NVIDIA DGX A100. The GPU performance seems degraded, and the system fans are operating loudly. What step should be recommended to identify and troubleshoot the hardware fault?
- A. Run a deep learning workload to stress test the GPUs and check whether the issue persists.
- B. Power drain then restart the DGX and check if the performance degradation resolves.
- C. Check the NVIDIA System Management Interface (nvidia-smi) for GPU status and temperatures.
- D. Increase the fan speed to maximum and check whether the performance improves.
Answer: C
Explanation:
nvidia-smi is the appropriate first diagnostic tool for checking GPU health, utilization, power, temperature, throttling, and error indicators. Since degraded performance and loud fans may point to thermal or GPU hardware issues, reviewing GPU status and temperatures helps identify the fault condition before taking corrective action.
NEW QUESTION # 114
During NVLink Switch configuration, you encounter issues where certain GPUs are not being recognized by the system. Which of the following troubleshooting steps are most likely to resolve this problem?
- A. Check the system BIOS settings to ensure that NVLink is enabled and configured correctly.
- B. Check the Power supply for enough capacity and stability.
- C. Ensure that the NVLink Switch firmware is compatible with the installed GPUs.
- D. Reinstall the operating system.
- E. Verify that all NVLink cables are securely connected and properly seated.
Answer: A,C,E
Explanation:
Explanation:Physical connection issues (A), BIOS configuration (B), and firmware incompatibility (C) are the most common causes of GPUs not being recognized. Reinstalling the operating system is a drastic measure that is unlikely to solve the problem. Checking the Power supply may also required to ensure the complete system have enough capacity and stability.
NEW QUESTION # 115
You are leading a project to enhance the energy efficiency of a data center that heavily relies on AI workloads. NVIDIA suggests moving beyond traditional metrics like Power Usage Effectiveness (PUE) to better capture the efficiency of modern data centers. Which strategy should you prioritize to develop more accurate energy-efficiency metrics?
- A. Use Power Usage Effectiveness as the primary metric while supplementing it with additional measures of useful work done per unit of energy.
- B. Focus on integrating kilowatt-hours into existing metrics to better reflect the actual energy used for productive work.
- C. Develop benchmarks tailored to specific workloads, such as MLPerf for AI applications, to better understand energy use in real-world scenarios.
- D. Use watts-used as the primary measure of efficiency, as it accurately reflects the power input at any given time.
Answer: C
Explanation:
The best strategy is to use workload-specific benchmarks such as MLPerf-style AI benchmarks to understand energy efficiency in real-world scenarios. NVIDIA has argued that traditional PUE is not enough for modern AI data centers because PUE measures facility overhead relative to IT power, but it does not measure useful computational output. For AI infrastructure, the important question is not only how much power the facility consumes, but how much useful AI work is completed per unit of energy. NVIDIA's discussion of next- generation efficiency metrics emphasizes useful work per energy and the need to account for real applications.
Kilowatt-hours are useful for measuring energy consumed, but they do not by themselves capture productive AI output. Watts-used is only instantaneous power and does not reflect completed work. PUE remains useful for facilities management, but relying on it as the primary metric misses the performance and efficiency characteristics of accelerated computing. Workload-specific benchmarks allow teams to compare training, inference, and system performance against energy consumed in practical AI operations.
NEW QUESTION # 116
A system administrator needs to enable MIG so that the end user can run multiple jobs on an NVIDIA A100 GPU. What command should be used?
- A. nvidia-smi -a100 -enablemig
- B. nvidia smi -i 0 --enablemig
- C. nvidia-smi -i 0 -mig 1
- D. nvidia -smi -i -mig 1 enable
Answer: C
Explanation:
nvidia-smi -i 0 -mig 1 enables MIG mode on GPU index 0. Once MIG mode is enabled, the A100 can be partitioned into multiple GPU instances so separate jobs can run with isolated GPU resources.
NEW QUESTION # 117
You notice that one of the fans in your GPU server is running at a significantly higher RPM than the others, even under minimal load. ipmitool sensor' output shows a normal temperature for that GPU. What could be the potential causes?
- A. The fan's PWM control signal is malfunctioning, causing it to run at full speed.
- B. The fan is attempting to compensate for restricted airflow due to dust buildup.
- C. A network connectivity issue is causing higher CPU utilization, leading to increased system-wide heat.
- D. The fan bearing is wearing out, causing increased friction and requiring higher RPM to maintain airflow.
- E. The server's BMC (Baseboard Management Controller) has a faulty temperature sensor reading, causing it to overcompensate.
Answer: A,B,D
Explanation:
A malfunctioning PWM control signal, worn fan bearings, or restricted airflow can all cause a fan to run at higher RPMs. While a faulty BMC sensor could be a cause, the question states that 'ipmitool sensor' shows a normal temperature. Network connectivity issues are less likely to cause an isolated fan to run high, if the GPU temperature is normal.
NEW QUESTION # 118
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