Valid NCP-AIO Test Sample - 100% High Pass-Rate Questions Pool

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NVIDIA AI Operations Sample Questions (Q70-Q75):

NEW QUESTION # 70
A user submits a Slurm job script with the following options:

Assuming each node has 4 GPUs, how many GPU resources will be allocated to this job across the entire cluster?

Answer: A

Explanation:
The job requests 2 nodes (nodes=2) and one GPU per node Therefore, a total of 2 GPUs (2 nodes 1 GPU/node) will be allocated to the job.


NEW QUESTION # 71
A long-running training job is unexpectedly terminated on a DGX server. After investigation, you find the following message in the system logs: 'OOM killer invoked'. What steps should you take to prevent this from happening again?

Answer: A,B,C,E

Explanation:
The 'OOM killer' indicates the system ran out of memory (RAM), not necessarily GPU memory. Reducing batch size (A) reduces memory consumption. Increasing swap space (B) provides more virtual memory. Proactive monitoring (C) helps identify memory bottlenecks before the OOM killer is invoked. Gradient accumulation (D) trades off computation for memory, reducing memory footprint. 'nvidia-smi' (E) manages GPU settings, not system RAM.


NEW QUESTION # 72
You are tasked with deploying NVIDIA Base Command Manager (BCM) on a Kubernetes cluster that utilizes NVIDIA GPUs for Ai workloads. The cluster already has the NVIDIA GPU Operator installed. Which of the following steps are crucial to ensure BCM can properly discover and manage the GPUs?

Answer: A,C,D

Explanation:
BCM relies on the NVIDIA Device Plugin to expose GPUs to Kubernetes. RBAC configuration is essential for BCM to interact with the cluster and manage GPU resources. The 'nvidia-driver-daemonset' ensures the necessary drivers are available within the pods where GPU workloads are executed. While a separate monitoring solution might provide additional insights, it's not strictly required for BCM's basic GPU discovery and management functionality. The CUDA toolkit is usually already present in the base images used for AI workloads.


NEW QUESTION # 73
You have successfully pulled a TensorFlow container from NGC and now need to run it on your stand-alone GPU-enabled server.
Which command should you use to ensure that the container has access to all available GPUs?

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When running a GPU-enabled container directly on a server with Docker, the flag--gpus allis required to allow the container access to all GPUs on the host system. This ensures that the TensorFlow container can utilize GPU resources fully. The other options either do not specify GPU access correctly or are Kubernetes- specific commands.


NEW QUESTION # 74
In an AI operations pipeline, which component is primarily responsible for continuously monitoring deployed models for performance degradation and automatically triggering retraining workflows when accuracy or latency metrics fall below predefined thresholds?

Answer: A

Explanation:
A model monitoring system tracks key production metrics such as accuracy, drift, and latency.
When thresholds are violated, it triggers alerts or retraining workflows. This ensures model performance remains stable and adapts to evolving real-world data conditions effectively.


NEW QUESTION # 75
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