An AI research team is experiencing slow model training times. Their performance monitoring indicates that the GPUs are frequently idle, waiting for data. They want to implement a single technology change to create a more direct data path between their storage and GPUs.
Their current setup is as follows:
Compute: Server with NVIDIA A100 GPUs
Storage: NetApp AFF A-Series (All-Flash)
Network: 100GbE Ethernet
Data_Path: Storage -> Host CPU/Memory -> GPU Memory
Which technology should the architect recommend to specifically address this data path inefficiency?

• A. NetApp SnapMirror
• B. NetApp FabricPool
• C. GPUDirect Storage
• D. A faster CPU in the server

A distributed training job running on the AIPod fails to start. The MLOps engineer inspects the events for one of the pending training pods and sees the following message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 5m12s default-scheduler 0/4 nodes are available: 4 node(s) had no available volume zone.
The PersistentVolumeClaim (PVC) for this pod specifies a StorageClass that uses the 'ontap-nas' Trident provisioner.
he Trident logs show no errors.
What is the most likely cause of this scheduling failure?

• A. The 'requiredTopology' in the StorageClass is misconfigured, preventing Trident from finding a valid ONTAP backend in the same zone as the available compute nodes.
• B. The training pod's container image does not exist in the registry.
• C. The Kubernetes nodes do not have the necessary NFS client utilities installed.
• D. The NetApp ASA storage system is offline.

An AI platform is suffering from poor performance during distributed training jobs. The training data resides on a single, large NFS volume. Monitoring shows that while the overall network throughput to the storage system is high, individual GPU nodes experience significant I/O wait times, and the single ONTAP volume is becoming a performance bottleneck. The goal is to re- architect the storage layout to maximize read parallelism and throughput for the training cluster.
Which two actions should the architect take to address this performance bottleneck? (Choose 2.)

• A. Enable QoS maximums on the training volume to limit its IOPS.
• B. Implement a NetApp FlexGroup volume to spread the dataset across multiple constituent volumes and aggregates.
• C. Increase the number of network ports connected to the storage controller.
• D. Replace the NFS protocol with iSCSI for all training data access.
• E. Use NetApp FlexCache to create a local cache of the training data on each compute node.

An AI platform administrator is trying to deploy a new GenAI toolkit using the BlueXP Workload Factory. The deployment fails, and the administrator examines the API response from BlueXP.
{
"jobId": "we-deploy-genai-987zy",
"status": "FAILED",
"statusCode": 403,
"message":
"Forbidden: The service principal or user account used by the Connector does not have the required permissions on the target subscription to create a new resource group.
Required permission: 'Microsoft.Resources/subscriptions/resourcegroups/write'." } Based on this API response, what is the root cause of the deployment failure?

• A. The Azure service principal associated with the BlueXP Connector lacks the necessary IAM role to create resource groups.
• B. The BlueXP Connector is offline and cannot communicate with the cloud provider.
• C. The selected ONTAP version does not support the GenAI toolkit.
• D. The GenAI toolkit requires a specific license that has not been added to the BlueXP digital wallet.

The AI training jobs on the AIPod are performing below expectations. The NVIDIA DGX servers' GPUs show low utilization. A performance analysis reveals that the bottleneck is not the storage system itself, but the network path between the storage and the compute nodes.
The current network configuration is as follows:
Network_Fabric: 100GbE Standard Ethernet
Protocol: NFS over TCP/IP
Data_Path: Storage -> Host CPU -> GPU Memory
Which network architecture enhancement would provide the most significant performance improvement by reducing latency and CPU overhead?

• A. Upgrade the network switches to a model with a larger packet buffer.
• B. Add more 100GbE network ports to the storage controllers.
• C. Implement RDMA over Converged Ethernet (RoCE) and configure GPUDirect Storage.
• D. Isolate the storage traffic on a separate VLAN from the management traffic.