NCP-AII Question 97
Select 4You are designing an AI cluster for an organization that runs both training and inference workloads. The training workloads involve processing large datasets, while the inference workloads require low-latency access to smaller datasets. What storage considerations should you prioritize in your cluster design?
- A
Implement a high-throughput storage solution capable of handling large datasets for training workloads.
- B
Use a low-latency storage solution optimized for inference workloads.
- C
Ensure the storage solution supports scalability to accommodate future data growth.
- D
Prioritize cost savings by using slower, archival storage for all workloads.
- E
Select a storage solution that integrates with the compute resources for efficient data access.
Show answer and explanation
Correct answers: A, B, C, E
Explanation
When designing storage for an AI cluster, it is important to balance the requirements of training and inference workloads. Training typically requires high-throughput storage for large-scale datasets, while inference demands low-latency storage for real-time processing. Scalability ensures future-proofing the design as data grows. Additionally, integrating storage with compute resources improves efficiency across the cluster. Cost-saving through slower archival storage is not suitable for these high-performance workloads.
- A. Correct.
High-throughput storage is essential for training workloads since they require access to large datasets, potentially involving parallel reads and writes.
- B. Correct.
Low-latency storage is critical for inference workloads because quick access to smaller datasets directly impacts real-time or near-real-time inference performance.
- C. Correct.
Scalability is a key consideration in cluster design to ensure that the storage solution can handle growing datasets and increasing demands over time.
- D. Incorrect.
Using slower, archival storage for all workloads would negatively impact both training and inference performance. While archival storage has its use cases, it is unsuitable for primary AI workloads.
- E. Correct.
Efficient integration between storage and compute resources minimizes bottlenecks, ensuring that data can be accessed quickly and seamlessly, which is vital for both training and inference.