NCP-AIO Administration Practice Test 3 — 15 Questions

Question 1

An administrator is configuring NVIDIA MIG (Multi-Instance GPU) on an A100 GPU for a multi-tenant AI inference environment. They need to ensure that each tenant gets isolated GPU resources and that memory bandwidth is partitioned. Which TWO actions must they perform? (Choose two.)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Enable MIG mode on the GPU using nvidia-smi -mig 1.. To configure MIG for multi-tenant isolation, the administrator must first enable MIG mode with nvidia-smi -mig 1. Then, they create GPU instances and compute instances using nvidia-smi mig -cgi and -cci, selecting profiles that partition memory and bandwidth. These two actions ensure each tenant gets dedicated, isolated GPU resources with guaranteed QoS.

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Configure SR-IOV on the GPU to partition memory bandwidth.

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Set the GPU to exclusive process mode using nvidia-smi -c 3.

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Enable MIG mode on the GPU using nvidia-smi mig -cgi.

Question 2

An administrator is setting up a shared DGX SuperPOD partition for multiple research groups. The requirement is that each group's jobs run with a guaranteed share of GPU resources and that no group can monopolize the partition during peak hours. The administrator also needs to enforce fair-share scheduling and preemption according to group quotas. Which NVIDIA software component should be configured to meet these requirements?

Accepted Answer

NVIDIA Base Command Manager with its built-in workload manager and fair-share policy configuration.. Base Command Manager provides the workload management and fair-share scheduling features needed to guarantee GPU resource shares, enforce quotas, and preempt jobs across research groups. The GPU Operator handles software stack lifecycle, DCGM handles monitoring, and Nsight Compute handles kernel profiling, so none of those alternatives can enforce multi-tenant scheduling policy on a shared SuperPOD partition.

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NVIDIA Nsight Compute with per-kernel resource profiling.

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NVIDIA DCGM with health checks and policy-based alerts.

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NVIDIA GPU Operator with node feature discovery and device plugin resource limits.

Question 3

An administrator needs to monitor GPU temperature and power usage across a cluster of NVIDIA GPUs in real time. Which NVIDIA tool provides this capability with a web-based dashboard?

Accepted Answer

NVIDIA Base Command Manager. NVIDIA Base Command Manager provides a centralized web-based dashboard for monitoring GPU metrics, including temperature and power usage, across a cluster. It aggregates data from all nodes and presents it in real time, enabling administrators to quickly assess cluster health. This built-in capability distinguishes it from command-line tools and operator frameworks.

Answer

nvidia-smi

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NVIDIA GPU Operator

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NVIDIA DCGM

Question 4

An administrator is configuring a multi-tenant NVIDIA AI Enterprise environment. Which mechanism is most effective for ensuring hardware-level isolation between concurrent training jobs on a single A100 GPU?

Accepted Answer

Multi-Instance GPU (MIG) partitioning. NVIDIA Multi-Instance GPU (MIG) allows a single physical GPU to be partitioned into multiple isolated instances, each with dedicated memory and compute cores. This is critical in multi-tenant AI environments because it prevents noisy neighbor issues, ensuring one workload's memory usage or compute demand does not degrade the performance of another. Proper resource partitioning is essential for maintaining strict SLAs and security boundaries within shared infrastructure.

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Kubernetes namespaces with resource quotas

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NVIDIA Driver process scheduling priority

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Docker container CPU pinning

Question 5

An administrator is optimizing a cluster for AI model training using NVIDIA Base Command. Which TWO tasks are critical for ensuring consistent performance across the training nodes?

Accepted Answer

Standardizing NVIDIA driver and NCCL library versions across all nodes. Consistency in high-performance AI clusters depends on hardware synchronization and resource availability. Ensuring that all nodes run identical driver and firmware versions prevents subtle performance regressions during multi-node training. Furthermore, configuring GPUDirect RDMA is essential for reducing latency in inter-GPU communication, which is a major bottleneck in distributed training jobs. These steps are fundamental to maintaining high utilization and predictable training times across large-scale NVIDIA accelerated infrastructure.

Answer

Increasing the size of the swap partition on each node

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Disabling the NVIDIA persistence mode to save power

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Reducing the number of CUDA streams per training job

Question 6

Refer to the exhibit. An administrator applies this security policy to a container runtime environment. What is the immediate effect on containerized AI applications within this scope?

Accepted Answer

The container is unable to detect or communicate with the NVIDIA GPU. The policy explicitly denies access to the character device files associated with the NVIDIA GPU. Without access to /dev/nvidia0, /dev/nvidiactl, and /dev/nvidia-uvm, the CUDA runtime cannot interact with the GPU hardware. Consequently, any attempt to initialize a CUDA device will fail, causing the application to crash. This policy is a common restrictive measure in high-security environments where GPU access must be strictly managed or audited.

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The container gains elevated privileges to access the host GPU

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The container can use the GPU but cannot perform memory mapping

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The container experiences increased latency for GPU operations

Question 7

When monitoring GPU health in an enterprise cluster, which command provides the most comprehensive snapshot of real-time power, temperature, and memory utilization?

Accepted Answer

nvidia-smi. The 'nvidia-smi' utility is the industry-standard tool for GPU management and monitoring. It provides a tabular overview of the system, including per-GPU power draw, thermal status, memory allocation, and active process lists. For administrators, this is the first point of contact for diagnosing performance bottlenecks or thermal throttling issues in a data center, making it the essential command for routine maintenance and operational health monitoring of NVIDIA hardware.

Answer

nvcc --version

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nvidia-bug-report.sh

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cat /proc/driver/nvidia/gpus/*/information

Question 8

An administrator notices that GPU utilization is high, but throughput in an AI training job remains low. What is the most likely bottleneck?

Accepted Answer

Data pipeline or storage I/O bottleneck. When GPU utilization is high but throughput is low, the GPU is likely stalling while waiting for data. This is typically a sign of an input/output (I/O) bottleneck, where the data pipeline (e.g., loading images from disk or network) cannot keep up with the GPU's processing speed. Ensuring the data preprocessing pipeline is sufficiently parallelized and optimized is crucial for maximizing GPU utilization and maintaining high training throughput in AI workloads.

Answer

Insufficient CUDA cores on the GPU

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Incompatible NVIDIA driver version

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Excessive usage of GPU registers

Question 9

Refer to the exhibit. An administrator observes this in the logs during a multi-GPU training job. What is the performance implication of this setting?

Accepted Answer

GPU-to-GPU data transfer performance is significantly reduced. Disabling Peer-to-Peer (P2P) communication forces NCCL to use system memory (often via the network or PCIe bus) to facilitate data movement between GPUs. This bypasses the high-bandwidth NVLink interconnect, resulting in significantly increased latency and lower throughput for collective operations. This configuration is typically used only for debugging or when hardware incompatibility prevents direct P2P access, as it severely hinders the performance of multi-GPU, multi-node AI training workloads.

Answer

The training job will have lower memory consumption

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The job will be more stable across nodes

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The system will utilize less power during training

Question 10

Which NVIDIA technology enables a GPU to be shared among multiple virtual machines or containers while maintaining strict hardware isolation?

Accepted Answer

NVIDIA Multi-Instance GPU (MIG). NVIDIA Multi-Instance GPU (MIG) is the core technology that enables hardware-level partitioning on supported GPUs. By creating dedicated instances that have their own memory and compute resources, MIG ensures that workloads remain isolated, providing predictable quality of service and security in multi-tenant environments. This is a foundational technology for maximizing GPU utilization in cloud and enterprise data center environments where mixed workloads are common.

Answer

NVIDIA CUDA streams

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NVIDIA Collective Communications Library (NCCL)

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NVIDIA GPUDirect RDMA

Question 11

An administrator needs to ensure that all GPU drivers are updated across a heterogeneous cluster without causing downtime. What is the best strategy?

Accepted Answer

Perform rolling updates by draining nodes one by one. Implementing a rolling update strategy allows the cluster to be updated one node at a time while the remaining nodes continue to process training jobs. By draining the target node of all active jobs, upgrading the drivers, and then re-adding the node to the production pool, the administrator maintains service availability. This is the standard practice in production AI operations to minimize disruption and ensure consistent driver versions across the entire fleet.

Answer

Simultaneously update all nodes during a maintenance window

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Use a containerized driver approach to avoid host updates

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Only update the driver on the master node

Question 12

Which THREE components are required for a container to successfully leverage NVIDIA GPUs on a Kubernetes cluster?

Accepted Answer

NVIDIA Container Toolkit. Successful GPU integration in Kubernetes relies on the interaction between the container engine, the NVIDIA driver, and the device plugin. The driver provides the kernel-level interface, the container runtime handles the low-level injection, and the device plugin advertises the hardware to the scheduler. These three parts must be correctly configured and compatible to ensure that containers can schedule and utilize GPUs effectively without manual configuration or persistent connectivity issues.

Answer

NVIDIA Data Center GPU Manager (DCGM)

Answer

Pre-installed CUDA Toolkit inside the container

Question 13

An administrator is configuring a new cluster and wants to ensure that telemetry data from GPUs is collected in a centralized manner. Which tool is best suited for this requirement?

Accepted Answer

NVIDIA DCGM Exporter. NVIDIA DCGM Exporter is the industry-standard tool for collecting GPU telemetry data in Kubernetes environments. It aggregates metrics from the Data Center GPU Manager and exposes them in a format compatible with Prometheus, allowing for centralized monitoring, visualization, and alerting. This visibility is essential for understanding cluster performance, identifying anomalies, and optimizing resource utilization in large-scale AI infrastructure deployments, making it the preferred choice for enterprise monitoring solutions.

Answer

nvidia-smi log file rotation

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Manual polling of /proc/driver/nvidia

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NVIDIA Nsight Systems

Question 14

An administrator wants to prevent unauthorized users from accessing sensitive model weights stored in GPU memory. Which security feature should be implemented to ensure hardware-level isolation of the memory space?

Accepted Answer

Implement NVIDIA Confidential Computing.. Hardware-level memory isolation via Confidential Computing technologies, such as NVIDIA Confidential Computing (CC), protects data in use by encrypting memory contents. For administrators handling sensitive IP, this provides a root-of-trust that persists even if the OS or hypervisor is compromised. This is a critical administrative control for ensuring compliance and data sovereignty in multi-tenant cloud environments where infrastructure is shared among different departments or organizations.

Answer

Enable standard Linux filesystem permissions.

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Use a simple SSH firewall rule.

Answer

Update the NVIDIA CUDA shared library path.

Question 15

Which administrative practice ensures that a cluster is prepared for the arrival of new NVIDIA GPU hardware with minimal downtime?

Accepted Answer

Update the OS and NVIDIA drivers across the cluster.. Maintaining an up-to-date driver and software stack via a robust package management system is the key to minimizing downtime during hardware upgrades. Administrators must ensure that the kernel and driver versions are compatible with the new hardware before it arrives. This proactive preparation is vital for maximizing cluster uptime and ensuring that researchers can immediately begin using the new hardware for their AI experiments without configuration delays.

Answer

Wait for the hardware to arrive before checking driver support.

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Manually install every CUDA library on all nodes.

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Modify the BIOS settings to ignore PCIe errors.