NVIDIA NCA-AIIO Online Practice Questions and Exam Preparation

NVIDIA NCA-AIIO Online Questions & Answers

• Question 21:

  What is the purpose of NVIDIA Base Command Manager?

  A. To orchestrate and monitor large-scale AI infrastructure
  B. To train deep learning models on edge devices
  C. To perform firmware updates on GPUs
  D. To manage user authentication for cloud services
  A. To orchestrate and monitor large-scale AI infrastructure

  ---

  Explanation

  NVIDIA Base Command Manager provides centralized orchestration, scheduling, and monitoring of AI training workloads across DGX and GPU clusters.

• Question 22:

  When monitoring a GPU-based workload, what is GPU utilization?

  A. The maximum amount of time a GPU will be used for a workload.
  B. The GPU memory in use compared to available GPU memory.
  C. The percentage of time the GPU is actively processing data.
  D. The number of GPU cores available to the workload.
  C. The percentage of time the GPU is actively processing data.

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  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.

  (

  NVIDIA AI Infrastructure and Operations Study Guide, Section on GPU Monitoring)

• Question 23:

  Which is the best PUE value for a data center?

  A. PUE of 1.2
  B. PUE of 3.5
  C. PUE of 5.0
  D. PUE of 2.0
  A. PUE of 1.2

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  Explanation

  Power Usage Effectiveness (PUE) measures data center efficiency, with an ideal value of 1.0 (all power used by IT equipment). A PUE of 1.2, indicating only 20% overhead, is highly efficient and closer to the ideal than 2.0 (100% overhead),

  3.5, or 5.0, making it the best among the options for energy-conscious AI deployments.

  (

  NVIDIA AI Infrastructure and Operations Study Guide, Section on Data Center Efficiency)

• Question 24:

  Which GPUs should be used when training a neural network for self-driving cars?

  A. NVIDIA H100 GPUs
  B. NVIDIA L4 GPUs
  C. NVIDIA DRIVE Orin
  A. NVIDIA H100 GPUs

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  Explanation

  Training neural networks for self-driving cars requires immense computational power and high-bandwidth memory to process vast datasets (e.g., sensor data, video). NVIDIA H100 GPUs, with their cutting-edge architecture and massive

  throughput, are ideal for these demanding workloads. L4 GPUs are optimized for inference and efficiency, while DRIVE Orin targets in-vehicle inference, not training, making H100 the best choice.

  (

  NVIDIA AI Infrastructure and Operations Study Guide, Section on GPU Selection for Training)

• Question 25:

  When deploying high-density workloads in a data center, what are the three main resource constraints that need to be considered?

  A. Processing speed, storage capacity, and network connectivity.
  B. Power, cooling, and physical space.
  C. Bandwidth, security, and redundancy.
  B. Power, cooling, and physical space.

  ---

  Explanation

  High-density workloads (e.g., GPU clusters for AI) strain data center resources, primarily power (to supply dense servers), cooling (to dissipate heat from tightly packed hardware), and physical space (to house equipment). While processing

  speed, bandwidth, and other factors matter, power, cooling, and space are the physical constraints most critical to deployment feasibility.

  (

  NVIDIA AI Infrastructure and Operations Study Guide, Section on Data Center Resource Constraints)

• Question 26:

  Which of the following aspects have led to an increase in the adoption of AI? (Choose two.)

  A. Moore’s Law
  B. Rule-based machine learning
  C. High-powered GPUs
  D. Large amounts of data
  C. High-powered GPUs
  D. Large amounts of data

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  Explanation

  The surge in AI adoption is driven by two key enablers: high-powered GPUs and large amounts of data. High-powered GPUs provide the massive parallel compute capabilities necessary to train complex AI models, particularly deep neural networks, by processing numerous operations simultaneously, significantly reducing training times. Simultaneously, the availability of large datasets-spanning text, images, and other modalities-provides the raw material that modern AI algorithms, especially data-hungry deep learning models, require to learn patterns and make accurate predictions. While Moore’s Law (the doubling of transistor counts) has historically aided computing, its impact has slowed, and rule-based machine learning has largely been supplanted by data-driven approaches. (

  NVIDIA AI Infrastructure and Operations Study Guide, Section on AI Adoption Drivers)

• Question 27:

  Which of the following NVIDIA tools is primarily used for monitoring and managing AI infrastructure in the enterprise?

  A. NVIDIA NeMo System Manager
  B. NVIDIA Data Center GPU Manager
  C. NVIDIA DGX Manager
  D. NVIDIA Base Command Manager
  D. NVIDIA Base Command Manager

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  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.

  (

  NVIDIA Base Command Manager Documentation, Overview Section)

• Question 28:

  How many Mellanox ConnectX-6 Single Port VPI cards are in a DGX A100 system?

  A. 8
  B. 16
  C. 4
  A. 8

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  Explanation

  The DGX A100 system includes eight Mellanox ConnectX-6 Single Port VPI cards, providing high-speed connectivity (up to 200 Gb/s) for clustering and data transfer. These cards support versatile protocols (InfiniBand or Ethernet), enabling

  robust multi-node AI workloads, with eight being the standard configuration for this system.

  (

  NVIDIA DGX A100 System Documentation, Networking Section)

• Question 29:

  The foundation of the NVIDIA software stack is the DGX OS. Which of the following Linux distributions is DGX OS built upon?

  A. Ubuntu
  B. Red Hat
  C. CentOS
  A. Ubuntu

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  Explanation

  DGX OS, the operating system powering NVIDIA DGX systems, is built on Ubuntu Linux, specifically the Long-Term Support (LTS) version. It integrates Ubuntu’s robust base with NVIDIA-specific enhancements, including GPU drivers, tools,

  and optimizations tailored for AI and high-performance computing workloads. Neither Red Hat nor CentOS serves as the foundation for DGX OS, making Ubuntu the correct choice.

  (

  NVIDIA DGX OS Documentation, System Requirements Section)

• Question 30:

  What is the importance of a job scheduler in an AI resource-constrained cluster?

  A. It allocates resources based on which job requests came first.
  B. It ensures that all jobs in the cluster are executed simultaneously.
  C. It increases the number of resources available in the cluster.
  D. It allocates resources efficiently and optimizes job execution.
  D. It allocates resources efficiently and optimizes job execution.

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  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.

  (

  NVIDIA AI Infrastructure and Operations Study Guide, Section on Job Scheduling Importance)