NVIDIA NCP-AIOL Dumps

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Exam Code	NCP-AIOL
Exam Name	NVIDIA-Certified Professional – AI Operations (NCP-AIOL)
Update Date	28 Aug, 2026
Total Questions	160 Questions Answers With Explanation

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Question # 1

A Slurm cluster administrator wants to ensure that a job submission script requests exactly 2 GPUs on a single compute node. Which directive should be included in the batch script to correctly specify this GPU resource requirement?

A. #SBATCH --gres=gpu:2
B. #SBATCH --gpus-per-task=2
C. #SBATCH --ntasks=2 --gpu=1
D. #SBATCH --resource=gpu:2

Answer: A
Explanation: In Slurm, Generic Resource (GRES) scheduling is used to request specialised hardware such as GPUs. The --gres flag is the standard directive for requesting GPU resources within a batch job script. The format gpu:N specifies the GPU resource type and the number of GPUs required on the allocated node.Option A is correct. The directive #SBATCH --gres=gpu:2 requests 2 GPU devices on the compute node allocated for the job. This is the standard and most widely used method for GPU resource allocation in batch scripts and is supported across Slurm-managed NVIDIA GPU clusters. Option B is incorrect. --gpus-per-task allocates GPUs relative to each task, not per node. For a single-node job with one task it may produce the same result, but it is not the standard node-level GPU request directive. Option C is incorrect. --ntasks=2 sets the number of parallel tasks, and there is no --gpu flag in standard Slurm syntax. This combination does not correctly specify a 2-GPU-per-node resource request. Option D is incorrect. --resource is not a valid Slurm directive for GPU allocation. Reference URLs:

https://docs.nvidia.com/datacenter/dps/versions/latest/guides/concepts/slurm/

https://slurm.schedmd.com/gres.html

Question # 2

A researcher submits a multi-GPU training job that requests 4 GPUs across 2 nodes using Slurm. The job immediately enters PENDING state and remains there for over an hour despite other single-GPU jobs running successfully. The cluster administrator needs to identify the detailed reason why the job is stuck. Which Slurm command provides the most complete diagnostic information for a specific pending job?

A. squeue -j <job_id>
B. sinfo --all
C. scontrol show job <job_id>
D. sacct -j <job_id>

Answer: C
Explanation: When a Slurm job remains in PENDING state, the administrator needs detailed diagnostic information beyond what squeue provides. The scontrol show job command returns the complete job record, including the Reason field, which specifies exactly why the job cannot be scheduled—for example, insufficient resources, partition limits, QoS constraints, or node failures.Option A is incorrect. The squeue command shows a brief listing of job status, including a short Reason code, but it does not provide the full diagnostic context needed to identify the root cause of a prolonged pending state. Option B is incorrect. sinfo displays the state of cluster partitions and nodes, not individual job scheduling reasons. It is useful for checking node availability but does not explain why a specific job is pending. Option C is correct. scontrol show job <job_id> displays the complete job record, including the detailed Reason field, resource request details, partition constraints, eligible time, and node requirements—all of which help diagnose why a multi-node GPU job is stuck in PENDING state. Option D is incorrect. sacct is used to report accounting information for completed or running jobs. For a job still in PENDING state, sacct may return limited or no useful diagnostic information. Reference URLs:

https://docs.nvidia.com/dgx-superpod/administration-guide-dgx-superpod/latest/managing-slurm.html

https://slurm.schedmd.com/scontrol.html

Question # 3

True or False: On NVIDIA Hopper architecture GPUs (such as the H100), enabling MIG mode requires a GPU reset and the MIG mode setting persists automatically across system reboots without any additional configuration.

A. False — On Hopper GPUs, enabling MIG mode no longer requires a GPU reset to take effect, and MIG mode is not persistent across reboots; it must be re-enabled after each reboot.
B. True — Hopper GPUs require a reset to enable MIG mode, and the setting is stored in GPU InfoROM so it persists across reboots automatically.
C. False — MIG mode on Hopper GPUs requires a full system reboot to activate, and the setting does persist in the InfoROM.
D. True — Hopper GPUs do not require a reset and MIG mode persists across reboots via InfoROM storage.

Question # 4

An administrator needs to enable MIG mode on an NVIDIA A100 40GB GPU and list the available GPU instance profiles before creating instances. Which command should be used to display all supported MIG profiles on the system?

A. nvidia-smi mig -lgip
B. nvidia-smi --query-gpu=mig.mode.current
C. dcgmi mig --list-profiles
D. nvml --show-mig-profiles

Answer: A
Explanation: The nvidia-smi command-line tool is the standard utility for managing MIG configuration. The -lgip flag stands for "list GPU instance profiles" and displays all supported MIG partitioning options for the detected GPUs, including profile names, memory sizes, and available slot counts.Option A is correct. The command nvidia-smi mig -lgip lists all available GPU instance profiles for each MIG-capable GPU on the system. This is the documented command for discovering which profiles can be created before proceeding with instance configuration. Option B is incorrect. This command queries whether MIG mode is currently enabled or disabled on the GPU. It does not list the available instance profiles that can be created. Option C is incorrect. dcgmi is the command-line interface for DCGM, which is used for health monitoring and diagnostics. It does not provide MIG profile listing functionality. Option D is incorrect. nvml is not a standalone command-line tool. NVML is a programmatic API. There is no nvml binary that accepts the --show-mig-profiles argument. Reference URLs:

https://docs.nvidia.com/datacenter/tesla/mig-user-guide/getting-started-with-mig.html

https://docs.nvidia.com/datacenter/tesla/mig-user-guide/

Question # 5

An AI platform team at a financial services firm is deploying an NVIDIA A100 80GB GPU to serve multiple concurrent inference workloads for different business units. Each business unit requires guaranteed memory and compute resources and must be isolated from other units so that one workload cannot affect the performance of another. The platform team must configure the GPU to meet these requirements without deploying additional physical hardware. Which approach correctly addresses this requirement?

A. Enable time-sliced GPU sharing so that each business unit's workload receives equal GPU scheduling time on the shared GPU.
B. Enable Multi-Instance GPU (MIG) mode on the A100 and create isolated GPU instances, each with dedicated compute slices, memory bandwidth, and L2 cache allocation.
C. Configure NVIDIA vGPU in pass-through mode to allocate the full GPU to a single virtual machine per business unit.
D. Use CUDA Multi-Process Service (MPS) to allow multiple processes to share the GPU compute engine concurrently.