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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA Certified Professional โ AI Infrastructure |
| Exam Number: | NCP-AII |
| Certificate Validity Period: | 2 years |
| Passing Score: | 700 (scale of 0-1000) |
| Available Languages: | English |
| Real Exam Qty: | 50 |
| Exam Duration: | 90 minutes |
| Exam Format: | Multiple Select, Multiple Choice |
| Related Certifications: | NCP-AI NCP-DES |
| Exam Price: | $195 USD |
| Sample Questions: | NVIDIA NCP-AII Sample Questions |
| Exam Way: | Online proctored exam (Pearson VUE) |
| Pre Condition: | Recommended: hands-on experience with NVIDIA AI infrastructure products; basic knowledge of Linux, networking, and data center operations |
| Official Syllabus URL: | https://www.nvidia.com/en-us/certifications/ncp-ai-infra/ |
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NEW QUESTION # 161
To validate bisectional bandwidth across two racks in a Spectrum-X Ethernet fabric, which NCCL test configuration isolates East-West traffic?
Answer: B
Explanation:
In a large-scale Spectrum-X Ethernet fabric, "East-West" traffic refers to the cross-rack communication between compute nodes. To validate the "Bisectional Bandwidth" (the throughput between two halves of the cluster), administrators use NCCL tests with specific environment variables to control traffic patterns. The NCCL_TESTS_SPLIT variable is used to partition the GPUs into distinct groups for the benchmark. Setting NCCL_TESTS_SPLIT="DIV 8" is a standard configuration for multi-node testing on 8-GPU systems. It effectively divides the total number of GPUs by the node count, creating a test environment where each GPU communicates with its corresponding rank on other nodes. By combining this with -g 1 (one GPU per process) across multiple nodes, the engineer can force data to travel across the leaf-and-spine switches rather than staying within the NVLink fabric of a single node. This isolates the physical network performance from the internal GPU-to-GPU bandwidth, providing a true measurement of the fabric's ability to handle high- speed AI traffic.
NEW QUESTION # 162
Consider the following Dockerfile snippet:
This Dockerfile is used to build a deep learning application. After building and running a container from this image, you observe that the application is not detecting the GPU. You have verified that the NVIDIA Container Toolkit is installed and configured correctly on the host. What is the most likely reason for this issue?
Answer: C
Explanation:
The 'docker run' command must include the =gpus all' (or equivalent) flag to explicitly request GPU resources for the container (C). The base image 'nvidia/cuda:ll .6.2-base-ubuntu20.04' provides the CUDA runtime, but the NVIDIA Container Toolkit on the host handles the GPU device mapping. The application code (B) doesn't need to explicitly request GPUs; the CUDA runtime will handle that. 'nvidia-pyindex' (D) is related to package management, not GPU detection. The Dockerfile includes a specific CUDA version that mitigates version differences (E). The base image does not include the Container Toolkit, which is installed on the HOST.
NEW QUESTION # 163
An engineer needs to verify the current firmware versions of all components (ATF, BSP, NIC, UEFI) on a BlueField-3 DPU's BMC. Which Redfish API command provides this information?
Answer: A
Explanation:
Modern NVIDIA BlueField DPUs include an integrated Baseboard Management Controller (BMC) that supports the industry-standardRedfish APIfor out-of-band management. While CLI tools like mlxconfig (Option A) or mstflint (Option C) can be used from the host OS to check the NIC firmware, they cannot easily query the BMC-specific components like the ARM Trusted Firmware (ATF), the Board Support Package (BSP), or the UEFI bootloader of the DPU. The Redfish standard specifies a common URI for hardware inventory. The FirmwareInventory endpoint (Option D) is the correct RESTful path to retrieve a comprehensive JSON object containing the versioning details for all firmware-controllable components on the DPU. This is the preferred method for automated data center management systems (like NVIDIA Base Command Manager) to verify that DPUs are at the correct "Golden Image" version during the staging phase.
Note that "FirmwareList" (Option B) is not a standard Redfish URI for this specific data.
NEW QUESTION # 164
A data scientist reports slow data loading times when training a large language model. The data is stored in a Ceph cluster. You suspect the client-side caching is not properly configured. Which Ceph configuration parameter(s) should you investigate and potentially adjust to improve data loading performance? Select all that apply.
Answer: B,D
Explanation:
Client-side caching in Ceph is primarily controlled by 'client cache size' which determines the amount of memory the Ceph client uses for caching data. 'mds cache size' controls the metadata server cache size, impacting metadata operations. controls the maximum number of background requests a FUSE client can make, influencing concurrency. affects the number of threads used by the OSDs, not the client-side caching, and 'client quota' limits storage usage, not caching.
NEW QUESTION # 165
You suspect a faulty NVIDIA ConnectX-6 network adapter in a server used for RDMA-based distributed training. Which commands or tools can you use to diagnose potential issues with the adapter's hardware and connectivity?
Answer: A,B,D,E
Explanation:
All options except E are relevant for diagnosing network adapter issues. 'Ispci -v' (A) verifies hardware detection. 'ibstat' (B) checks InfiniBand-specific details. 'ethtoor (C) examines Ethernet settings. 'ping' (D) tests basic connectivity. 'nvsmimonitord' (E) focuses on GPU monitoring, not network adapters.
NEW QUESTION # 166
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