NVIDIA NCA-AIIO Online Practice Questions and Exam Preparation

NVIDIA NCA-AIIO Online Questions & Answers

• Question 41:

  Which aspect of computing uses large amounts of data to train complex neural networks?

  A. Machine learning
  B. Deep learning
  C. Inferencing
  B. Deep learning

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  Explanation

  Deep learning, a subset of machine learning, relies on large datasets to train multi-layered neural networks, enabling them to learn hierarchical feature representations and complex patterns autonomously. While machine learning

  encompasses broader techniques (some requiring less data), deep learning’s dependence on vast data volumes distinguishes it. Inferencing, the application of trained models, typically uses smaller, real-time inputs rather than extensive

  training data.

  (

  NVIDIA AI Infrastructure and Operations Study Guide, Section on Deep Learning Fundamentals)

• Question 42:

  Which NVIDIA technology enables secure, high-speed communication between GPUs across multiple nodes?

  A. NVLink
  B. GPUDirect RDMA
  C. Magnum IO
  D. NVSwitch
  B. GPUDirect RDMA

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  Explanation

  GPUDirect RDMA allows direct memory access between GPUs across nodes via RDMA-capable NICs, reducing CPU involvement and latency. It’s key for distributed AI workloads requiring multi-node communication.

• Question 43:

  When should RoCE be considered to enhance network performance in a multi-node AI computing environment?

  A. A network that experiences a high packet loss rate (PLR).
  B. A network with large amounts of storage traffic.
  C. A network that cannot utilize the full available bandwidth due to high CPU utilization.
  C. A network that cannot utilize the full available bandwidth due to high CPU utilization.

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  Explanation

  RoCE (RDMA over Converged Ethernet) enhances network performance by offloading data transport to the NIC via RDMA, bypassing CPU involvement. It’s particularly valuable when high CPU utilization limits bandwidth usage, as it reduces

  overhead and unlocks full link capacity. While RoCE can handle storage traffic, it’s less effective with high packet loss (requiring reliable networks), making CPU-bound scenarios its prime use case.

  (

  NVIDIA Networking Documentation, Section on RoCE Benefits)

• Question 44:

  What is the maximum number of MIG instances that an H100 GPU provides?

  A. 7
  B. 8
  C. 4
  A. 7

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  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.

  (

  NVIDIA H100 GPU Documentation, MIG Section)

• Question 45:

  A customer is evaluating an AI cluster for training and is questioning why they should use a large number of nodes. Why would multi-node training be advantageous?

  A. The model is too large to fit into GPU memory.
  B. The model is being used by a large number of users.
  C. The model is being used for large-scale inference workloads.
  A. The model is too large to fit into GPU memory.

  ---

  Explanation

  Multi-node training is advantageous when a model’s size-its parameters, activations, and gradients-exceeds the memory capacity of a single GPU. By sharding the model across multiple nodes (using techniques like data parallelism or model

  parallelism), training becomes feasible and efficient. User count and inference scale are unrelated to training architecture needs, which focus on compute and memory distribution.

  (

  NVIDIA AI Infrastructure and Operations Study Guide, Section on Multi-Node Training Benefits)

• Question 46:

  Which two components are included in GPU Operator? (Choose two.)

  A. Drivers
  B. PyTorch
  C. DCGM
  D. TensorFlow
  A. Drivers
  C. DCGM

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  Explanation

  The NVIDIA GPU Operator is a tool for automating GPU resource management in Kubernetes environments. It includes two key components: GPU drivers, which provide the necessary software to interface with NVIDIA GPUs, and the

  NVIDIA Data Center GPU Manager (DCGM), which offers health monitoring, telemetry, and diagnostics for GPU clusters. Frameworks like PyTorch and TensorFlow are separate AI development tools, not part of the GPU Operator, which

  focuses on infrastructure rather than application layers.

  (

  NVIDIA GPU Operator Documentation, Components Section)

• Question 47:

  Which NVIDIA command-line tool is used to check GPU memory usage and temperature?

  A. gpustat
  B. nvidia-smi
  C. nvtop
  D. cuda-info
  B. nvidia-smi

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  Explanation

  The nvidia-smi tool provides detailed GPU statistics including memory, temperature, utilization, and process information for monitoring GPU health.

• Question 48:

  What is one key advantage that Cloud GPU Infrastructure has over On-Prem GPU infrastructure?

  A. Lower cost barrier to entry.
  B. Reduced cost of I/O traffic.
  C. Greater flexibility for hardware orchestration.
  A. Lower cost barrier to entry.

  ---

  Explanation

  Cloud GPU infrastructure lowers the cost barrier to entry by offering a pay-as-you-go model, eliminating the need for significant upfront capital expenditure on hardware. While on-prem may offer I/O cost savings or hardware control, the

  cloud’s accessibility and reduced initial investment make it a compelling choice for organizations seeking immediate GPU access without large sunk costs.

  (

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

• Question 49:

  In a multi-GPU AI cluster, which of the following tools is most suitable for job scheduling?

  A. NetQ
  B. Slurm
  C. DCGM
  D. Nsight Systems
  B. Slurm

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  Explanation

  Slurm is the industry-standard job scheduler for HPC and AI clusters, managing GPU allocation, queueing, and workload distribution.

• Question 50:

  What is the name of NVIDIA’s SDK that accelerates machine learning?

  A. Clara
  B. RAPIDS
  C. cuDNN
  C. cuDNN

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  Explanation

  The CUDA Deep Neural Network library (cuDNN) is NVIDIA’s SDK specifically designed to accelerate machine learning, particularly deep learning tasks. It provides highly optimized implementations of neural network primitives-such as

  convolutions, pooling, normalization, and activation functions-leveraging GPU parallelism. Clara focuses on healthcare applications, and RAPIDS accelerates data science workflows, but cuDNN is the core SDK for machine learning

  acceleration.

  (

  NVIDIA cuDNN Documentation, Introduction)