NCP-AII Question 55
Single answerYou are tasked with designing a high-performance AI data center. The data center needs to support low-latency communication for distributed deep learning workloads, while also being scalable to accommodate future growth. Which network topology would best meet these requirements?
- A
Fat-Tree Topology
- B
Bus Topology
- C
Ring Topology
- D
Star Topology
Show answer and explanation
Correct answer: A
Explanation
The Fat-Tree topology is the preferred choice for AI data centers because it provides high bandwidth and low latency, which are critical for distributed AI training and inference tasks. Additionally, its hierarchical structure supports scalability, allowing the data center to grow as demands increase, while maintaining efficient communication across nodes.
- A. Correct.
The Fat-Tree topology is widely used in modern data centers due to its low-latency, high-bandwidth characteristics, and scalability. It is ideal for distributed AI workloads because it minimizes bottlenecks and ensures efficient communication between nodes.
- B. Incorrect.
The Bus topology is outdated and not suitable for high-performance AI workloads. It lacks scalability and can suffer from significant communication bottlenecks as more devices are added.
- C. Incorrect.
The Ring topology is not optimal for AI data centers because it introduces latency with increased nodes. Data must traverse multiple hops, making it unsuitable for high-speed, low-latency requirements.
- D. Incorrect.
The Star topology is simple and easy to implement but is not scalable for large data centers. Additionally, it has a single point of failure, making it less reliable for AI workloads.