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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 5	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.

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NVIDIA AI Infrastructure Sample Questions (Q36-Q41):

NEW QUESTION # 36
Which of the following techniques can be used to optimize storage performance for deep learning training?

A. Using a larger block size for the file system
B. Data prefetching
C. Data deduplication
D. Data compression using lossless algorithms (e.g., gzip)
E. Data sharding

Answer: A,B,E

Explanation:
Data prefetching anticipates future data needs and loads data into the cache before it is requested. A larger block size can improve I/O throughput for large files. Data sharding distributes data across multiple storage devices to increase parallelism. Data compression, while saving space, can add overhead during training. Data deduplication is not normally usefull for training data sets.

NEW QUESTION # 37
You're deploying a multi-GPU training job on a cluster using Slurm. You need to ensure that the GPUs allocated to the job are healthy and functioning correctly before the training starts. What's the MOST effective approach to pre-validate the GPU hardware?

A. Monitor the GPU temperature using 'nvidia-smi' during the first few minutes of the training job.
B. Allocate all available GPUs to the job and assume they are healthy.
C. Execute the NVIDIA Data Center GPU Manager (DCGM) diagnostic suite on the allocated GPUs.
D. Check the output of 'nvidia-smi' to ensure all GPUs are listed and have the expected memory.
E. Run a simple CUDA vector addition program on each GPU and check for errors.

Answer: C

Explanation:
Using the DCGM diagnostic suite is the most thorough way to pre-validate GPU hardware. DCGM provides comprehensive tests to check GPU health, including memory, compute, and interconnects. A simple CUDA program or checking nvidia-smr provides basic validation, but not as comprehensive as DCGM. Monitoring temperature is reactive, not proactive. Assuming GPUs are healthy without validation is risky.

NEW QUESTION # 38
Which function is used to collect the cluster counters information?

A. SM
B. PM
C. GM
D. FM

Answer: B

Explanation:
The correct answer is PM, which refers to the Performance Manager or performance-management function used for collecting performance counter information from the InfiniBand fabric. In NVIDIA AI infrastructure, cluster counters are essential for troubleshooting and optimization because they expose link-level and port- level behavior across switches, HCAs, and fabric paths. These counters can include transmitted and received data, symbol errors, link errors, congestion indicators, XmitWait, packet drops, and other telemetry used to detect performance degradation. SM, or Subnet Manager, is responsible for fabric discovery, LID assignment, routing, and maintaining the logical InfiniBand subnet, but it is not primarily the performance-counter collection function. GM and FM are not the standard answers for collecting cluster counters in this context.
NVIDIA UFM Telemetry and diagnostic tooling collect and monitor InfiniBand port statistics such as bandwidth, congestion, errors, and latency, while UFM diagnostic output also includes PM counter dumps. In AI clusters, these counters help identify cabling faults, congestion, degraded links, and issues that can reduce NCCL and RDMA performance.

NEW QUESTION # 39
After initial setup and health checks, the DGX H100 system administrator wants to verify that containers can access GPUs before running production workloads. Which method is recommended for this validation?

A. sudo docker run --gpus all --rm nvcr.io/nvidia/cuda:12.1.1-base-ubuntu22.04 systemctl
B. sudo docker run --gpus all --rm nvcr.io/nvidia/cuda:12.1.1-base-ubuntu22.04 ls-la
C. sudo docker run --rm nvcr.io/nvidia/cuda:12.1.1-base-ubuntu22.04 nvidia-smi
D. sudo docker run --gpus all --rm nvcr.io/nvidia/cuda:12.1.1-base-ubuntu22.04 nvidia-smi

Answer: D

Explanation:
Running a CUDA container with --gpus all and executing nvidia-smi is the standard validation that Docker, the NVIDIA container runtime, drivers, and GPU access are working correctly before production workloads are launched.

NEW QUESTION # 40
A system administrator needs to enable MIG so that the end user can run multiple jobs on an NVIDIA A100 GPU. What command should be used?

A. nvidia-smi -i 0 -mig 1
B. nvidia smi -i 0 --enablemig
C. nvidia-smi -a100 -enablemig
D. nvidia -smi -i -mig 1 enable

Answer: A

Explanation:
nvidia-smi -i 0 -mig 1 enables MIG mode on GPU index 0. Once MIG mode is enabled, the A100 can be partitioned into multiple GPU instances so separate jobs can run with isolated GPU resources.

NEW QUESTION # 41
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