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2026 Marvelous NCP-AII: 100% NVIDIA AI Infrastructure Correct Answers

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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 4	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 5	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.

NVIDIA AI Infrastructure Sample Questions (Q77-Q82):

NEW QUESTION # 77
Consider a scenario where you are running a CUDA application on an NVIDIA GPU. The application compiles successfully but crashes during runtime with a *CUDA ERROR ILLEGAL ADDRESS* error. You've carefully reviewed your code and can't find any obvious out- of-bounds memory accesses. What advanced debugging techniques could help you pinpoint the source of this error?

A. Use 'cuda-memcheck' to detect memory access errors at runtime.
B. Employ the CUDA Debugger (cuda-gdb) to step through the code and inspect variable values and memory contents.
C. Enable ECC (Error Correction Code) memory on the GPU to detect and correct memory errors.
D. Reduce the block size used in CUDA kernels to decrease the likelihood of shared memory conflicts.
E. Utilize NVIDIA Nsight Systems to profile the application and identify memory allocation patterns.

Answer: A,B,E

Explanation:
'cuda-memcheck' (A) is specifically designed to detect memory access errors. 'cuda-gdb' (B) allows for detailed code inspection. NVIDIA Nsight Systems (C) provides profiling information that can help identify memory allocation issues that lead to the error. Enabling ECC (D) might mask the symptom but doesn't fix the underlying code error, also ECC is enabled by default, so that's a red herring. Reducing block size (E) is only relevant if shared memory is involved and potentially overflowing.

NEW QUESTION # 78
You're optimizing an Intel Xeon server with 4 NVIDIA GPUs for inference serving using Triton Inference Server. You've deployed multiple models concurrently. You observe that the overall throughput is lower than expected, and the GPU utilization is not consistently high.
What are potential bottlenecks and optimization strategies? (Select all that apply)

A. Insufficient CPU cores to handle the model loading and preprocessing requests. Increase the number of Triton instance groups for CPU-based models.
B. The GPUs are underutilized due to small batch sizes. Implement dynamic batching to increase batch sizes.
C. The models are memory-bound. Reduce the model precision (e.g., FP32 to FP16 or INT8).
D. Model loading and unloading overhead. Use model ensemble or dynamic batching to reduce frequency.
E. Insufficient PCle bandwidth between CPU and GPIJs. Reconfigure PCle lanes to improve bandwidth allocation to each GPIJ.

Answer: A,B,C,D

Explanation:
Multiple factors can contribute to low throughput in inference serving. Model loading overhead is significant, and dynamic batching is crucial to maximize throughput. Insufficient CPU cores and memory constraints on the GPU also limit performance. Model precision reduction helps reduce memory footprint and increase throughput. While PCle bandwidth is a factor, it is often not the primary bottleneck in inference serving.

NEW QUESTION # 79
You've installed a server with multiple NVIDIAAIOO GPUs intended for use with Kubernetes and NVIDIA's GPU Operaton After installing the GPU Operator, you notice that the GPUs are not being properly detected and managed by Kubernetes. Which of the following are potential causes and troubleshooting steps you should take?

A. The 'nvidia-docker2 runtime is not set as the default runtime in '/etc/docker/daemon.json' . Change the default runtime to 'nvidia' and restart the Docker daemon.
B. The Kubernetes nodes are not labeled correctly to indicate the presence of NVIDIA GPUs. Use 'kubectl label node nvidia.com/gpu.present=true'.
C. The NVIDIA Container Toolkit is not installed on the Kubernetes nodes. Install the toolkit according to NVIDIA's documentation.
D. The NVIDIA drivers are not properly installed on the host operating system before installing the GPU Operator. Verify the driver installation using 'nvidia-smr.
E. The GPU Operator's configuration is incorrect, preventing it from properly discovering and managing the GPUs. Check the GPU Operator's logs and configuration files.

Answer: B,C,D,E

Explanation:
All the options are valid reasons. The NVIDIA driver must be present on the host, the nodes need to be labelled to be recongnized by the Kubernetes, container tookit is required for running GPU enabled container and configuration of GPU operator must be correct.

NEW QUESTION # 80
You are troubleshooting a network performance issue in your NCP-AII environment. After running 'ibstat' on a host, you see the following output for one of the InfiniBand ports:

What does the 'LMC: 0' indicate, and what are the implications for network performance?

A. LMC: 0 indicates that the link is down and not functioning correctly.
B. LMC : 0 indicates that the Subnet Manager is not running correctly.
C. LMC : 0 is the default and expected value; it has no impact on performance.
D. LMC: 0 indicates that Link Aggregation (LAG) is not enabled on this port, meaning only a single link is being used for communication.
E. LMC : 0 indicates the port is operating at the lowest possible speed.

Answer: D

Explanation:
LMC (Link Mask Capability) indicates the level of link aggregation enabled. LMC: 0 means that link aggregation is disabled. This means that only one link is being used for communication, which could limit the overall bandwidth and potentially create a bottleneck if multiple GPUs are trying to communicate through that single link. Higher LMC values indicate that multiple links are aggregated together to increase bandwidth.

NEW QUESTION # 81
A user reports that their CUDA application is running slower than expected after an NVIDIA driver update. You suspect a driver compatibility issue. How can you revert to a previous NVIDIA driver version on an Ubuntu system, assuming you have the older driver package 'nvidia-driver-470 470.82.00-0ubuntu1_amd64.deb'?

A. First, remove the current NVIDIA driver using 'sudo apt purge nvidia- s, then install the .deb' package using 'sudo dpkg -i nvidia-driver-470_470.82.00-Oubuntu1_amd64.deb' and resolve any dependency issues with 'sudo apt --fix-broken install'.
B. Edit the '/etc/apt/sources.list' file to point to an older repository containing the desired driver version, then update and upgrade the system.
C. Simply install the .deb' package using 'sudo dpkg -i
D. Use the NVIDIA System Management Interface Cnvidia-smi') to downgrade the driver.
E. Install the older .run' installer version, if you previously installed it using .run', using the -uninstall option first.

Answer: A

Explanation:
The safest and most reliable approach is to first remove the current NVIDIA driver using 'sudo apt purge nvidia- to avoid conflicts. Then, install the .deb' package using 'sudo dpkg -i' and resolve any potential dependency issues with 'sudo apt -fix-broken install'. 'nvidia-smi' cannot downgrade drivers. Editing "/etc/apt/sources.list' can be risky and lead to system instability. Directly installing with 'dpkg -i' without purging the old driver can cause conflicts. If you installed with .run' before, then you must uninstall using that method first.

NEW QUESTION # 82
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100% NCP-AII Correct Answers | NCP-AII Exam Fees

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2026 Marvelous NCP-AII: 100% NVIDIA AI Infrastructure Correct Answers

NVIDIA NCP-AII Exam Syllabus Topics:

NVIDIA AI Infrastructure Sample Questions (Q77-Q82):

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