The correct answer is: Use Amazon S3 for raw data storage. Provision an Amazon FSx for Lustre file system with persistent SSD storage and configure it to import data from and export data to the S3 bucket. Mount the file system on all EC2 instances.

Amazon FSx for Lustre is purpose-built for high-performance computing (HPC) and analytics workloads. When backed by persistent SSD storage, FSx for Lustre can achieve sub-millisecond latencies and exceed 67 GBps throughput, making it ideal for scenarios involving large-scale parallel processing by hundreds of EC2 instances. The ability to link with Amazon S3 for seamless data import and export also adds flexibility and scalability.

The option Deploy an Amazon FSx for NetApp ONTAP file system with the tiering policy for all volumes set to NONE. Import the dataset into the file system and mount it on each EC2 instance does not meet the performance requirements. While FSx for ONTAP supports NFS and SMB access and provides good general-purpose performance, it is not designed for HPC workloads with strict latency and throughput requirements like FSx for Lustre is.

The option Store the data in an Amazon S3 bucket. Attach an Amazon FSx for Lustre file system with persistent HDD storage to the EC2 instances, and enable S3 import/export functionality is also unsuitable. The use of HDD-based storage does not meet the sub-millisecond latency and high throughput requirement. Persistent HDD provides cost-efficient capacity, not the performance needed for this scenario.

The option Create an Amazon FSx for NetApp ONTAP file system and set the tiering policy for all volumes to ALL. Mount the file system to the EC2 fleet after uploading the research data uses a tiering policy that shifts cold data to capacity pool storage, which is slower and introduces additional latency. This again does not satisfy the strict latency and throughput demands of the application.

This question is essential because it tests the candidates ability to choose the right AWS storage service for high-throughput, low-latency workloadsparticularly in compute-intensive scientific environments. A useful trick is to remember that FSx for Lustre is the go-to for HPC and large-scale analytics, while FSx for NetApp ONTAP is better for enterprise workloads needing traditional NFS/SMB storage features.

The challenge in this question lies in carefully comparing similar-sounding services and recognizing the difference between HDD and SSD-based persistent storage within FSx for Lustre, and understanding the performance impact of NetApp ONTAP's tiering policies. The key is aligning the workload's latency and throughput needs with the correct storage backend.

Full AWS Practitioner Certification Question