NVIDIA NCA-AIIO Exam Prep
AI Infrastructure and Operations (Page 3 )

Updated On: 28-Aug-2026

Which of the following statements is true about GPUs and CPUs?

  1. GPUs are optimized for parallel tasks, while CPUs are optimized for serial tasks.
  2. GPUs have very low bandwidth main memory while CPUs have very high bandwidth main memory.
  3. GPUs and CPUs have the same number of cores, but GPUs have higher clock speeds.
  4. GPUs and CPUs have identical architectures and can be used interchangeably.

Answer(s): A

Explanation:

GPUs consist of many simpler cores designed for massive parallelism (e.g., thousands of threads), whereas CPUs use a few powerful cores optimized for sequential and branching workloads.



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

  1. Drivers
  2. PyTorch
  3. DCGM
  4. TensorFlow

Answer(s): A,C

Explanation:

The NVIDIA GPU Operator automates the deployment of the GPU device driver and the NVIDIA Data Center GPU Manager (DCGM) for health monitoring and telemetry.



Which phase of deep learning benefits the greatest from a multi-node architecture?

  1. Data Augmentation
  2. Training
  3. Inference

Answer(s): B

Explanation:

Training deep learning model, especially large-scale ones, sees the biggest gains from spreading computation (forward/backward passes and gradient synchronization) across multiple nodes, dramatically reducing total time to convergence.



Which architecture is the core concept behind large language models?

  1. BERT Large model
  2. State space model
  3. Transformer model
  4. Attention model

Answer(s): C

Explanation:

The Transformer model underpins modern large language models, using self-attention layers and feed-forward networks in a stacked, encoder-decoder (or decoder-only) architecture to capture long-range dependencies efficiently.



What is a key value of using NVIDIA NIMs?

  1. They provide fast and simple deployment of AI models.
  2. They have community support.
  3. They allow the deployment of NVIDIA SDKs.

Answer(s): A

Explanation:

NVIDIA NIMs are packaged, GPU-accelerated inference microservices with industry-standard APIs that simplify and accelerate deploying AI models across clouds, data centers, and edge - enabling fast, turnkey AI inference without hand-crafting deployment pipelines.



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