Questions for the NCA-AIIO were updated on : Nov 21 ,2025
What is a significant benefit of using containers in an AI development environment?
B
Explanation:
Containers (e.g., Docker) encapsulate AI applications with their dependencies, ensuring consistent
execution across diverse environments—from development laptops to production clusters—without
manual reconfiguration. They don’t inherently improve model accuracy, generate datasets, or boost
GPU speed, focusing instead on portability and reproducibility. (Note: The document incorrectly lists
A; B is correct per NVIDIA standards.)
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Containers in AI
Development)
What is the maximum number of MIG instances that an H100 GPU provides?
A
Explanation:
The NVIDIA H100 GPU supports up to 7 Multi-Instance GPU (MIG) partitions, allowing it to be divided
into seven isolated instances for multi-tenant or mixed workloads. This capability leverages the
H100’s architecture to maximize resource flexibility and efficiency, with 7 being the documented
maximum.
(Reference: NVIDIA H100 GPU Documentation, MIG Section)
How is out-of-band management utilized by network operators in an AI environment?
A
Explanation:
Out-of-band management provides a dedicated channel, separate from the production network, for
remotely managing and troubleshooting devices (e.g., switches, servers) in an AI environment. This
ensures control and recovery even if the primary network fails, unlike options tied to model training,
compute power, or traffic prioritization.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Out-of-Band
Management)
Which NVIDIA tool aids data center monitoring and management?
D
Explanation:
NVIDIA Data Center GPU Manager (DCGM) aids data center monitoring and management by
providing detailed GPU telemetry, health diagnostics, and performance tracking at scale. Clara
targets healthcare, TensorRT optimizes inference, and Mellanox Insight isn’t a standard NVIDIA tool,
making DCGM the go-to solution.
(Reference: NVIDIA DCGM Documentation, Overview Section)
What is the importance of a job scheduler in an AI resource-constrained cluster?
D
Explanation:
In a resource-constrained AI cluster, a job scheduler (e.g., Slurm) efficiently allocates limited
resources (GPUs, CPUs) to workloads, optimizing utilization and job execution time. It prioritizes
based on policies, not just first-come-first-served, and doesn’t add resources or run all jobs
simultaneously, focusing instead on resource optimization.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Job Scheduling
Importance)
When monitoring a GPU-based workload, what is GPU utilization?
C
Explanation:
GPU utilization is defined as the percentage of time the GPU’s compute engines are actively
processing data, reflecting its workload intensity over a period (e.g., via nvidia-smi). It’s distinct from
memory usage (a separate metric), core counts, or maximum runtime, providing a direct measure of
compute activity.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on GPU Monitoring)
Which NVIDIA software provides the capability to virtualize a GPU?
B
Explanation:
NVIDIA vGPU (Virtual GPU) software enables GPU virtualization by partitioning a physical GPU into
multiple virtual instances, assignable to virtual machines or containers for accelerated workloads.
Horizon is a VMware product, and “virtGPU” isn’t an NVIDIA offering, confirming vGPU as the correct
solution.
(Reference: NVIDIA vGPU Documentation, Overview Section)
Which of the following NVIDIA tools is primarily used for monitoring and managing AI infrastructure
in the enterprise?
D
Explanation:
NVIDIA Base Command Manager is an enterprise-grade platform for monitoring, orchestrating, and
managing AI infrastructure at scale, including DGX clusters and cloud resources. It offers unified
visibility and workflow automation. DCGM focuses on GPU monitoring, DGX Manager is system-
specific, and NeMo System Manager is fictional, making Base Command Manager the enterprise
solution.
(Reference: NVIDIA Base Command Manager Documentation, Overview Section)
What is a common tool for container orchestration in AI clusters?
A
Explanation:
Kubernetes is the industry-standard tool for container orchestration in AI clusters, automating
deployment, scaling, and management of containerized workloads. Slurm manages job scheduling,
Apptainer (formerly Singularity) runs containers, and MLOps is a practice, not a tool, making
Kubernetes the clear leader in this domain.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Container
Orchestration)
What is the primary command for checking the GPU utilization on a single DGX H100 system?
A
Explanation:
The nvidia-smi (System Management Interface) command is the primary tool for checking GPU
utilization on NVIDIA systems, including the DGX H100. It provides real-time metrics like utilization
percentage, memory usage, and power draw. NVML (NVIDIA Management Library) is an API, not a
command, and ctop is unrelated, solidifying nvidia-smi as the standard.
(Reference: NVIDIA DGX H100 System Documentation, Monitoring Section)
What NVIDIA tool should a data center administrator use to monitor NVIDIA GPUs?
C
Explanation:
The NVIDIA Data Center GPU Manager (DCGM) is the recommended tool for data center
administrators to monitor NVIDIA GPUs. It provides real-time health monitoring, telemetry (e.g.,
utilization, temperature), and diagnostics, tailored for large-scale deployments. NetQ focuses on
network monitoring, and there’s no “NVIDIA System Monitor” in this context, making DCGM the
correct choice. (Note: The document incorrectly lists D; C is intended.)
(Reference: NVIDIA DCGM Documentation, Overview Section)
In an AI cluster, what is the importance of using Slurm?
D
Explanation:
Slurm (Simple Linux Utility for Resource Management) is a workload manager critical for AI clusters,
handling job scheduling and resource allocation. It ensures tasks are assigned to available
GPUs/CPUs efficiently, supporting scalable training and inference. It doesn’t manage storage,
perform training, or interconnect nodes—those are separate functions.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Slurm in AI Clusters)
In an AI cluster, what is the purpose of job scheduling?
C
Explanation:
Job scheduling in an AI cluster assigns workloads (e.g., training, inference) to available compute
resources (GPUs, CPUs), optimizing resource utilization and ensuring efficient execution. It’s distinct
from data analysis, monitoring, or software management, focusing solely on workload distribution.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Job Scheduling)
Which of the following statements is true about Kubernetes orchestration?
B, D
Explanation:
Kubernetes excels in container orchestration with advanced scheduling (assigning workloads based
on resource needs and availability) and load balancing (distributing traffic across pods via Services).
It’s not inherently bare-metal (it runs on various platforms), and inferencing capability depends on
applications, not Kubernetes itself, making B and D the true statements.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Kubernetes
Orchestration)
In a data center, what is the purpose and benefit of a DPU?
D
Explanation:
A Data Processing Unit (DPU) is a programmable processor that offloads, accelerates, and isolates
infrastructure workloads—like networking, storage, and security—from the CPU. This enhances
performance, reduces CPU overhead, and improves security by segregating tasks, benefiting AI data
centers. It doesn’t handle backups or physical infrastructure directly, focusing instead on compute
efficiency.
(Reference: NVIDIA DPU Documentation, Overview Section)