Q: 7
You are managing an AI project for a healthcare application that processes large volumes of medical
imaging data using deep learning models. The project requires high throughput and low latency
during inference. The deployment environment is an on-premises data center equipped with NVIDIA
GPUs. You need to select the most appropriate software stack to optimize the AI workload
performance while ensuring scalability and ease of management. Which of the following software
solutions would be the best choice to deploy your deep learning models?
Options
Discussion
Option A Official practice tests and the exam guide both highlight TensorRT for workloads like this.
It’s A. TensorRT is specifically made to optimize deep learning inference on NVIDIA GPUs, which is exactly what this healthcare imaging app needs for performance. Docker (B) is only managing containers, not actually boosting inference speed, so that's the trap option here. Pretty sure about this but open if someone sees a missed use case for B.
A . Similar exam questions and official study guide both focus on TensorRT for this use case.
A imo. If it said anything about training/new frameworks, C could be right instead.
I get why A looks right, but I'm thinking C here since MXNet can handle both training and inference and gives flexibility if you need more than just raw speed. Seems like a trap to ignore C.
A tbh. C is tempting but it's more for model development. TensorRT really stands out for GPU inference here.
Yeah, I’m picking A. TensorRT is just what you want for high-performance inference on NVIDIA GPUs, especially with medical imaging. The other options don’t directly optimize the models for GPU inference like this does. Not 100 percent sure but haven’t seen a more fitting choice here, agree?
Its A, not B. Docker's for containers, but TensorRT is really built for GPU-optimized inference and that's the performance trap here. Pretty sure on this, but let me know if you see it differently.
A tbh
Feels like B. Docker makes deploying and scaling super easy across an on-prem data center, and you can bundle everything up for reproducibility. While A (TensorRT) is great for raw performance, for ease of management and scalability Docker seems a better fit to me. It's possible I'm missing a detail about GPU optimization though.
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