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NVIDIA NCP-AII Exam Overview:

Certification Vendor:	NVIDIA
Exam Name:	NVIDIA AI Infrastructure (NCP-AII) Certification Exam
Exam Number:	NCP-AII
Exam Format:	Multiple-choice, Scenario-based questions
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)
Exam Duration:	120 minutes
Real Exam Qty:	70-75
Available Languages:	English
Certificate Validity Period:	2 years
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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NCP-AII Latest Study Notes - Study Guide NCP-AII Pdf

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NVIDIA NCP-AII Exam Syllabus Topics:

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	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 3	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 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	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.

NVIDIA AI Infrastructure Sample Questions (Q89-Q94):

NEW QUESTION # 89
A team is installing the NVIDIA Run:ai control plane on a Kubernetes cluster. Which two options are most critical to validate before proceeding?

A. All cluster nodes have NVIDIA GPUs installed.
B. Ensure Kubernetes is running on the cluster.
C. Helm is installed on the installer machine.
D. NTP is disabled to simplify time synchronization.

Answer: B,C

Explanation:
Installing the NVIDIA Run:ai control plane requires a functioning Kubernetes cluster as the target platform and Helm on the installer machine to deploy the control plane charts. These prerequisites must be validated before starting the installation.

NEW QUESTION # 90
A system administrator needs to check the health of the DGX power supplies. What command should be used?

A. ipmitool sdr |grep PWR_SYSTEM
B. nvidia-smi -q|grep -i power\ draw
C. lspci |grep -i psu
D. nvsm show psus

Answer: D

Explanation:
nvsm show psus displays the DGX power supply status and health information. It is the appropriate NVSM command to check whether the power supplies are present, healthy, and operating normally.

NEW QUESTION # 91
You have a dataset with many small files (e.g., images). Directly reading these files can result in high metadata access overhead. What are the MOST effective strategies to mitigate this problem?

A. Replicate the dataset to increase read availability
B. Increase the number of metadata servers in the parallel file system
C. Combine small files into larger archive files (e.g., using TAR or HDF5)
D. Use a key-value store (e.g., LevelDB, RocksDB) to store the file data and metadata
E. Store the files on a file system with very small block sizes

Answer: B,C,D

Explanation:
Combining small files reduces the number of metadata operations. A key-value store can provide efficient access to small data items. Increasing metadata servers reduces the load on individual metadata servers. Smaller block sizes exacerbate the problem. Replicating data doesn't address metadata overhead.

NEW QUESTION # 92
You've replaced a faulty NVIDIA Quadro RTX 8000 GPU with an identical model in a workstation. The system boots, and 'nvidia-smi' recognizes the new GPU. However, when rendering complex 3D scenes in Maya, you observe significantly lower performance compared to before the replacement. Profiling with the NVIDIA Nsight Graphics debugger shows that the GPU is only utilizing a small fraction of its available memory bandwidth. What are the TWO most likely contributing factors?

A. The new GPU's PCle link speed is operating at a lower generation (e.g., Gen3 instead of Gen4).
B. The NVIDIA OptiX denoiser is not properly configured or enabled.
C. The workstation's power plan is set to 'Power Saver,' limiting GPU performance.
D. The newly installed GPU's VBIOS has not been properly flashed, causing an incompatibility issue.
E. The Maya scene file contains corrupted or inefficient geometry.

Answer: A,C

Explanation:
Low memory bandwidth utilization after replacing a GPU suggests a bottleneck in data transfer or power delivery. A lower PCle link speed (A) would severely limit the GPU's ability to receive data from the CPU and system memory, resulting in underutilization. Setting the power plan to 'Power Saver' (C) restricts the GPU's power budget, preventing it from reaching its maximum clock speed and memory bandwidth. While OptiX denoiser configuration (B) and scene file issues (D) can impact rendering performance, they are less likely to directly cause low memory bandwidth utilization. VBIOS issues (E) could cause problems, but incorrect or missing VBIOS can cause driver errors or complete system unresponsiveness.

NEW QUESTION # 93
During a 72-hour HPL burn-in test on a DGX H100 cluster, one node shows a 15% performance drop after 48 hours. What are the two most likely causes and diagnostic steps? (Choose two.)

A. Thermal throttling due to cooling issues; check nvidia-smi dmon
B. Network packet loss; analyze ibdiagnet reports
C. MPI configuration error; rerun with --cpu-affinity adjustments.
D. Memory corruption; reboot the node and reduce problem size (N)

Answer: A,B

Explanation:
A delayed performance drop during a long HPL burn-in is commonly caused by thermal throttling as cooling conditions degrade under sustained load, so GPU temperature, clocks, power, and throttling behavior should be checked with nvidia-smi dmon. Network packet loss or fabric errors can also reduce multi-node HPL performance over time, so ibdiagnet reports should be analyzed for link errors, packet loss, retries, or degraded InfiniBand paths.

NEW QUESTION # 94
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