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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 Price:	$400
Exam Duration:	120 minutes
Exam Format:	Multiple-choice, Scenario-based questions
Available Languages:	English
Certificate Validity Period:	2 years
Real Exam Qty:	70-75
Related Certifications:	NVIDIA-Certified Associate AI Infrastructure and Operations (NCA-AIIO) NVIDIA-Certified Professional AI Operations (NCP-AIO) NVIDIA-Certified Professional AI Networking (NCP-AIN)
Recommended Training:	AI Infrastructure & Operations Fundamentals (NVIDIA Training) AI Infrastructure Professional Workshop
Exam Registration:	Official NVIDIA Certification Portal NVIDIA AI Infrastructure Certification Page
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 Reliable Practice Materials, NCP-AII Questions

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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 4	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 5	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.

NVIDIA AI Infrastructure Sample Questions (Q66-Q71):

NEW QUESTION # 66
You are using MIG (Multi-lnstance GPU) on an NVIDIAAI 00 GPU within a Kubernetes cluster. You want to configure a pod to use a specific MIG instance. How do you define the GPU resource request in the pod's YAML definition?

A. Use a generic resource request like 'nvidia.com/gpu: 0.125' to allocate a portion of the GPIJ.
B. Specify the full GPU ID (e.g., 'nvidia.com/gpu: 1') in the resource request.
C. Specify the MIG device profile name (e.g., 'nvidia.com/mig-l g.7gb: 1 in the resource request.
D. Use a custom resource definition (CRD) to define MIG instances and reference it in the resource request.
E. Using the annotation 'nvidia.com/gpu.product' and the value of the particular GPU device you desire.

Answer: C

Explanation:
When using MIG, you need to specify the MIG device profile name in the resource request to allocate a specific MIG instance to the pod. For example, 'nvidia.com/mig-1g.7gb: 1' requests one instance of the lg.7gb MIG profile. Specifying the full GPU ID will not work with MIG. Generic resource requests do not work with MIG.

NEW QUESTION # 67
You are tasked with installing the latest NVIDIA driver on a server running Ubuntu 22.04 for A1 workloads. You have downloaded the driver package 'NVIDIA-Linux-x86 64-535.104.05.run'. Before installation, what is the most critical step to ensure a smooth process, assuming secure boot is enabled?

A. Ensure the 'nouveau- driver is blacklisted by adding 'blacklist nouveau- to 'letc/modprobe.d/blacklist-nouveau.conf.
B. Simply run the ' .run' file using 'sudo ./NVlDlA-Linux-x86_64-535.104.05.run' .
C. Disable Secure Boot in the BIOS before installing the driver.
D. Install the driver using 'apt install nvidia-driver-535' and let the system handle Secure Boot automatically.
E. Create a DKMS module and sign the driver with a machine owner key (MOK) for Secure Boot compatibility.

Answer: A,E

Explanation:
Secure Boot requires kernel modules to be signed. Directly running the .run' file or disabling Secure Boot are generally not recommended. DKMS and MOK signing allows the driver to be validated by the system. Blacklisting nouveau ensures that it won't conflict with the NVIDIA driver. Installing via apt doesn't guarantee Secure Boot compatibility without further steps, so the best approach involves DKMS/MOK and blacklisting.

NEW QUESTION # 68
You are installing multiple NVIDIA GPUs in a server for a deep learning cluster. To optimally utilize the GPUs, which software component(s) are MANDATORY after the physical installation and driver setup? (Select TWO)

A. A text editor.
B. NVIDIA CUDA Toolkit.
C. A web browser.
D. A spreadsheet program.
E. A deep learning framework like TensorFlow or PyTorch.

Answer: B,E

Explanation:
The NVIDIA CUDA Toolkit provides the necessary libraries and tools for GPU-accelerated computing. A deep learning framework (TensorFlow or PyTorch) is required to build and train deep learning models that leverage the GPUs. While a web browser and text editor might be useful, they are not mandatory. Spreadsheet applications have no purpose here.

NEW QUESTION # 69
Which of the following is the MOST critical consideration when planning the cooling strategy for a server rack containing multiple NVIDIA A100 GPUs?

A. Applying thermal paste to the GPU memory chips.
B. Ensuring the server room temperature is kept below 25 degrees Celsius.
C. Increasing the fan speed of the server chassis fans to maximum.
D. Optimizing airflow to ensure hot air is efficiently exhausted from the rack and cool air is drawn in.
E. Using liquid cooling for the CPUs, but air cooling for the GPUs.

Answer: D

Explanation:
While ambient temperature is important, optimized airflow is crucial for removing the heat generated by the GPUs. Focusing solely on CPU cooling neglects the GPU heat. Applying thermal paste to memory chips is generally unnecessary unless specifically recommended by the manufacturer. Maximizing fan speed can help, but efficient airflow design is more effective.

NEW QUESTION # 70
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. Simply install the .deb' package using 'sudo dpkg -i
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. Install the older .run' installer version, if you previously installed it using .run', using the -uninstall option first.
D. 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'.
E. Use the NVIDIA System Management Interface Cnvidia-smi') to downgrade the driver.

Answer: D

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 # 71
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