Online Access Free NS0-901 Practice Test

What is the primary reason for implementing a hybrid cloud architecture in this medical imaging scenario?

• A.To simplify network management by using a single, global virtual network spanning the on- premises and cloud environments.
• B.To leverage the powerful, elastic GPU compute resources available in the public cloud for model training while keeping sensitive source data on-premises to meet security and HIPAA compliance requirements.
• C.To exclusively use cloud-native AI services, eliminating the need for any on-premises hardware.
• D.To reduce the overall cost of storage by keeping all data in the public cloud.

Which of the following applications use AI in the healthcare industry? (Choose two)

• A.Automating financial reporting
• B.Diagnosing diseases
• C.Managing hospital supply chains
• D.Predicting patient outcomes

An architect is designing the storage and network infrastructure for a new, large-scale AI cluster dedicated to training foundational models. The primary design goal is to achieve the highest possible data throughput and the lowest latency to ensure multi-million dollar GPU resources are never idle. Which two technologies are essential to include in the design to achieve this goal?
(Choose 2.)

• A.A 10GbE Ethernet network for all data traffic.
• B.A tiered storage architecture using NetApp FabricPool.
• C.NetApp StorageGRID as the primary storage for the training datasets.
• D.An InfiniBand or RoCE-capable Ethernet network fabric.
• E.GPUDirect Storage support on the storage system.

What is the primary architectural benefit of using technologies like RDMA (Remote Direct Memory Access) and GPUDirect Storage in a high-performance AI training cluster?

• A.They enable the use of lower-cost Ethernet switches in place of InfiniBand fabrics without any performance degradation.
• B.They automatically encrypt data in-flight between the GPU servers and the storage system.
• C.They allow GPUs to communicate directly with each other and with NVMe storage, bypassing the host CPU and system memory, which significantly reduces I/O latency and CPU overhead.
• D.They reduce the power consumption of the storage array by offloading checksum calculations.

An MLOps team is using the BlueXP Workload Factory to automate the deployment of a Jupyter notebook environment for a new data scientist. The deployment job fails. The team reviews the job details in the BlueXP UI.
Job ID: 78a1c-2b3d-4e5f
Service: BlueXP Workload Factory
Action: Deploy JupyterHub on Kubernetes
Status: FAILED
Error Message: "Failed to create PersistentVolumeClaim 'jupyter-pvc-user01'.
User 'service-account- bluexp' does not have permission to 'create' resource
'persistentvolumeclaims' in cluster 'k8s-prod- cluster-01'."
What is the most direct way to resolve this issue?

• A.Manually create the PersistentVolumeClaim using 'kubectl'.
• B.Restart the BlueXP Connector to refresh its permissions.
• C.Modify the Kubernetes RBAC (Role-Based Access Control) configuration to grant the 'service- account-bluexp' the necessary permissions to create PVCs in the target cluster.
• D.Increase the storage quota for the 'jupyter-pvc-user01' PVC.