NVIDIA NCA-AIIO Exam Prep
AI Infrastructure and Operations (Page 2 )

Updated On: 17-Sep-2026

A company is implementing a new network architecture and needs to consider the requirements and considerations for training and inference.
Which of the following statements is true about training and inference architecture?

  1. Training architecture and inference architecture have the same requirements and considerations.
  2. Training architecture is only concerned with hardware requirements, while inference architecture is only concerned with software requirements.
  3. Training architecture is focused on optimizing performance while inference architecture is focused on reducing latency.
  4. Training architecture and inference architecture cannot be the same.

Answer(s): C

Explanation:

Training architectures prioritize maximizing throughput and model convergence speed (e.g., by scaling across GPUs or specialized accelerators), whereas inference architectures are designed primarily to minimize response latency (often via model optimization, batching strategies, and edge deployment).



For which workloads is NVIDIA Merlin typically used?

  1. Recommender systems
  2. Natural language processing
  3. Data analytics

Answer(s): A

Explanation:

NVIDIA Merlin is an end-to-end framework purpose-built for developing and deploying large-scale recommender systems, handling data preprocessing, model training, and inference optimizations specifically for recommendation workloads.



Which NVIDIA parallel computing platform and programming model allows developers to program in popular languages and express parallelism through extensions?

  1. CUDA
  2. CUML
  3. CUGRAPH

Answer(s): A

Explanation:

CUDA is NVIDIA’s core parallel computing platform and programming model, providing language extensions in C, C++, Fortran (and bindings for Python, etc.) so developers can express and manage parallelism directly.



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

  1. Moore’s Law.
  2. Rule-based machine learning.
  3. High Powered GPUs.
  4. Large amounts of data.

Answer(s): C,D

Explanation:

High-powered GPUs provide the parallel compute needed to train complex models efficiently, and the availability of large datasets supplies the rich information that modern AI methods require to learn effectively.



In training and inference architecture requirements, what is the main difference between training and inference?

  1. Training requires real-time processing, while inference requires large amounts of data.
  2. Training requires large amounts of data, while inference requires real-time processing.
  3. Training and inference both require large amounts of data.
  4. Training and inference both require real-time processing.

Answer(s): B

Explanation:

Training workflows demand access to vast datasets to optimize model parameters, whereas inference systems are architected for real-time or low-latency responses.



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