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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA AI Infrastructure (NCP-AII) Certification Exam |
| Exam Number: | NCP-AII |
| Certificate Validity Period: | 2 years |
| Related Certifications: | NVIDIA-Certified Professional AI Operations (NCP-AIO) NVIDIA-Certified Associate AI Infrastructure and Operations (NCA-AIIO) NVIDIA-Certified Professional AI Networking (NCP-AIN) |
| Available Languages: | English |
| Exam Format: | Scenario-based questions, Multiple-choice |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | 70-75 |
| Exam Price: | $400 |
| Recommended Training: | AI Infrastructure & Operations Fundamentals (NVIDIA Training) AI Infrastructure Professional Workshop |
| Exam Registration: | NVIDIA AI Infrastructure Certification Page Official NVIDIA Certification Portal |
| Sample Questions: | NVIDIA NCP-AII Sample Questions |
| Exam Way: | Online proctored exam (remote) or authorized test center depending on region |
| Pre Condition: | Recommended 2–3 years of experience working in data center environments with NVIDIA hardware solutions (GPU servers, networking, storage). |
| Official Syllabus URL: | https://www.nvidia.com/en-eu/learn/certification/ai-infrastructure-professional/ |
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NEW QUESTION # 157
A user wants to restrict a Docker container to use only GPUs 0 and 2. Which command achieves this?
Answer: B
Explanation:
With the advent of the NVIDIA Container Toolkit and modern Docker versions (19.03+), the --gpus flag is the official, verified method for resource allocation. To restrict a container to specific hardware IDs, the syntax requires a specific string format: --gpus '"device=0,2"'. This tells the NVIDIA Container Runtime to map only those specific physical GPU devices into the container's namespace. While environment variables like NVIDIA_VISIBLE_DEVICES (Option B) were used in older "nvidia-docker2" setups, they are now considered legacy and can be overridden by the more modern --gpus flag. Option D is incorrect because simply mapping the device nodes (/dev/nvidiaX) is insufficient; the container also needs the appropriate volume mounts for the NVIDIA drivers and libraries, which the --gpus flag handles automatically. This precise isolation is critical in multi-tenant AI environments to ensure that a single developer or job doesn't accidentally utilize the entire 8-GPU tray of a DGX H100.
NEW QUESTION # 158
After successfully installing the NVIDIA Container Toolkit and configuring Docker, you're attempting to build a container image that leverages the GPU. You're using a Dockerfile but encounter the following error during the 'docker build' process: 'error during connect: this error may indicate that the docker daemon is not running'. However, the Docker daemon IS running. What is the most likely reason the build process is failing to connect, specifically in the context of GPU-enabled containers?
Answer: B
Explanation:
The error 'error during connect: this error may indicate that the docker daemon is not running' during a 'docker build', when the daemon actually is running, can indicate a failure to connect to the daemon for a specific reason related to GPU access. 'docker build' requires - gpus all' to be passed in order for CUDA to correctly build the image. Permissions(A) are unlikely to cause this specific connection error. User group issues(B) are usually related to running containers, not building them. A networking issue (D) is possible but less likely in the context of a local build. The same can be said for container exceeding the hosts' available memory.
NEW QUESTION # 159
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?
Answer: B
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 # 160
After upgrading NVIDIA GPU Operator, Kubernetes reports all worker nodes as Ready, but newly created GPU workloads remain in the Pending state. The administrator confirms that GPUs are healthy and visible to the operating system. Which troubleshooting step should be performed first?
Answer: A
Explanation:
If GPUs are detected by the operating system but Kubernetes cannot schedule GPU workloads, the Device Plugin is one of the first components to verify. It advertises GPU resources to the Kubernetes scheduler. Healthy hardware alone does not guarantee that GPUs are available as schedulable resources within the cluster.
NEW QUESTION # 161
A system administrator has upgraded the firmware of the DPU. What will be the state of the firmware after the upgrade?
Answer: B
Explanation:
The correct answer is that the firmware is waiting on reboot to become active. For NVIDIA networking devices and DPUs, a firmware upgrade generally writes the new firmware image to the device flash, but the currently running firmware may remain active until the device is reset, rebooted, or power-cycled. NVIDIA MFT documentation notes that loading new firmware may require mlxfwreset, reboot, or power cycle, depending on the device and situation. This distinction is important during server bring-up because administrators must verify both the flashed firmware version and the running firmware version before declaring the update complete. Assuming the firmware is immediately active can lead to version drift across nodes, inconsistent behavior, or failed validation. The firmware is not deleted, and saying it is merely copied but not installed is incomplete because the update process has written it to the device. The operational issue is activation. In production clusters, firmware upgrades should be followed by the required reset procedure and post-update validation using appropriate NVIDIA tools before returning the node to service.
NEW QUESTION # 162
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