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?
Answer(s): C
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?
Answer(s): A
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?
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.)
Answer(s): C,D
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?
Answer(s): B
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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