NCP-AII Prüfungsfragen Prüfungsvorbereitungen 2026: NVIDIA AI Infrastructure - Zertifizierungsprüfung NVIDIA NCP-AII in Deutsch Englisch pdf downloaden

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NVIDIA NCP-AII Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Networking and Storage Configuration | 20% | - NVIDIA networking solutions
- 1. BlueField DPU configuration
- 2. InfiniBand and Ethernet fabric setup
- Storage integration
- 1. Parallel file systems and object storage
- 2. Storage performance for AI workloads
|
| System and Server Bring-up | 20% | - Firmware and system configuration
- 1. BMC, BIOS, TPM and firmware updates
- 2. OS installation and base configuration
- Hardware installation and validation
- 1. Power, cooling and physical connectivity verification
- 2. Server, GPU, network and storage components setup
|
| GPU Resource Management | 15% | - Multi-Instance GPU (MIG) configuration
- 1. Partitioning and resource allocation
- 2. Isolation and performance tuning
- GPU scheduling and optimization
- 1. NVLink and fabric management
- 2. Workload placement and sharing
|
| Validation, Troubleshooting and Optimization | 20% | - Cluster validation and benchmarking
- 1. HPL, NCCL and performance testing
- 2. Health checks and error detection
- Troubleshooting and maintenance
- 1. Hardware and software fault isolation
- 2. Performance optimization and best practices
|
| Software Stack Deployment | 25% | - Orchestration and workload management
- 1. NGC catalog and software deployment
- 2. Slurm, Kubernetes and container orchestration
- NVIDIA software components
- 1. GPU drivers, container toolkit and runtime
- 2. Base Command Manager and cluster management tools
|
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NVIDIA AI Infrastructure NCP-AII Prüfungsfragen mit Lösungen (Q63-Q68):
63. Frage
You have installed NVIDIA drivers using the .run' installer on a system running Ubuntu. However, after each kernel update, the NVIDIA drivers stop working. What is the most effective way to address this issue permanently?
- A. Create a script that symlinks the old kernel modules to the new kernel directory.
- B. Use DKMS (Dynamic Kernel Module Support) to automatically rebuild the NVIDIA kernel modules after each kernel update.
- C. Manually compile the NVIDIA kernel modules after each kernel update.
- D. Disable automatic kernel updates.
- E. Re-run the '.run' installer after each kernel update.
Antwort: B
Begründung:
DKMS (Dynamic Kernel Module Support) is the most effective solution. It automatically rebuilds kernel modules (like NVIDIA's) whenever a new kernel is installed. Re-running the Irun' installer is a manual and error-prone process. Disabling kernel updates is not recommended for security reasons. Manually compiling is complex and time-consuming. Symlinking old modules is unlikely to work due to kernel API changes.
64. Frage
You are deploying an NVIDIA GPU-accelerated application in a virtualized environment using vGPU. How does vGPU technology impact power and cooling considerations compared to a bare-metal deployment, and what specific monitoring metrics become crucial?
- A. vGPU deployments eliminate the need for GPU monitoring, as the virtualization layer handles all power and cooling management.
- B. vGPU deployments have no significant impact on power and cooling requirements compared to bare-metal. Standard GPU temperature and power draw metrics are sufficient.
- C. vGPU deployments typically require less power and cooling than bare-metal, as resources are shared. The host server's overall power consumption becomes the primary monitoring metric.
- D. vGPU deployments require specialized cooling solutions that are not needed for bare metal setups.
- E. vGPU deployments can lead to higher overall power consumption and concentrated heat generation on the host server due to resource consolidation. Monitoring metrics like GPU utilization per VM, vGPU frame rate, and host server thermal headroom become crucial.
Antwort: E
Begründung:
vGPU deployments can increase overall power consumption and heat generation because multiple virtual machines share physical GPUs, concentrating the load on the host server. Monitoring GPU utilization per VM, vGPU frame rate, and host server thermal headroom becomes crucial to ensure optimal performance and prevent overheating. Host server thermal headroom refers to the difference between the maximum allowed temperature and the current operating temperature.
65. Frage
You are an infrastructure engineer tasked with validating a new AI training cluster before releasing it to users. Your team wants to perform a NeMo burn-in to ensure both hardware and software are reliable and ready for production workloads. Which of the following actions are required as part of a proper NeMo burn-in process? (Choose two.)
- A. Test inference using the NeMo API and approve the environment if the model outputs valid predictions.
- B. Download a pre-trained NeMo model and use it for a quick accuracy check on a user dataset, then consider the burn-in complete if results are reasonable.
- C. Run the configured NeMo training job repeatedly or for an extended duration, monitoring for errors, stalls, or performance drops across all GPUs and nodes.
- D. Configure a representative NeMo training or pretraining recipe and set up an executor to launch the job across intended nodes and GPUs.
Antwort: C,D
Begründung:
A proper NeMo burn-in uses a representative NeMo training or pretraining workload configured to run across the target GPUs and nodes. Running that workload for an extended duration while monitoring errors, stalls, GPU behavior, and performance stability validates that the hardware, drivers, containers, networking, and software stack are production-ready.
66. Frage
A user encounters "permission denied" errors when running GPU-accelerated containers on a Secure Boot- enabled system. What resolves this?
- A. Run Docker with sudo for elevated privileges.
- B. Enroll the MOK and sign NVIDIA kernel modules.
- C. Disable SELinux to relax unnecessary security policies.
- D. Reinstall Docker without the NVIDIA runtime.
Antwort: B
Begründung:
On systems where UEFI Secure Boot is enabled, the Linux kernel strictly enforces a "Signature Verification" policy for all loaded kernel modules. The NVIDIA driver consists of several modules (like nvidia.ko and nvidia-uvm.ko) that provide the interface between the hardware and the NVIDIA Container Runtime. If these modules are not signed by a key trusted by the system's firmware, the kernel will block them from loading, leading to "Permission Denied" errors when the container attempts to access /dev/nvidiactl or the GPU device nodes. To resolve this without compromising the security of the host, the administrator must utilize aMachine Owner Key (MOK). The modules must be signed using this key, and the key itself must be "Enrolled" into the system's Secure Boot database via the MOK management interface during the boot process. This establishes a "Chain of Trust" that allows the drivers to function. Disabling SELinux (Option C) or using sudo (Option D) will not resolve this, as the blockage is happening at the kernel-firmware interface level, not at the user-space permission level.
67. Frage
You are configuring a BlueField-2 DPU for link aggregation (bonding) with two 25GbE ports. After configuring the bond interface, you notice that traffic is not being distributed across both links. What are the two most likely causes of this issue? (Select TWO)
- A. The firewall on the DPU is blocking outgoing traffic on one of the interfaces.
- B. The switch connected to the DPU does not support the bonding mode configured on the DPU.
- C. The bonding mode is set to 'balance-alb' and the ARP monitoring interval is too high.
- D. The MTU size is different on the bond interface and the physical interfaces.
- E. The physical interfaces are not configured with the same speed and duplex settings.
Antwort: B,E
Begründung:
Bonding requires compatible configurations on both the server (DPU) and the switch. If the switch doesn't support the bonding mode, traffic won't be distributed correctly. Also, the speed and duplex settings must match on all interfaces in the bond for it to function properly. Mismatched MTU can cause packet fragmentation and performance issues but is less likely to prevent traffic distribution entirely. While ALB mode and ARP monitoring can impact load balancing, they're not the primary suspects. Firewalls might block traffic, but it is less related to the DPU setup.
68. Frage
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