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

The pod running the vector database on the Kubernetes cluster fails to start. An MLOps engineer runs 'kubectl describe pod vector-db-pod-0' and sees the following event message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 30s default-scheduler 0/8 nodes are available: 8 node(s) did not match pod anti-affinity rules.
The pod's manifest contains the following 'affinity' definition:
affinity:
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- labelSelector:
matchExpressions:
- key: app
operator: In
values:
- vector-db
topologyKey: "kubernetes.io/hostname"
What is the most likely reason the pod cannot be scheduled?

• A. The PersistentVolumeClaim for the pod is in a 'Pending' state.
• B. The 'vector-db-pod-0' is requesting more GPU resources than are available on any node.
• C. The 'topologyKey' is invalid; it should be 'failure-domain.beta.kubernetes.io/zone'.
• D. The pod's anti-affinity rule prevents it from being scheduled on any node that is already running another pod with the label 'app=vector-db'.

Due to the success of the "Advisor Assistant," the number of concurrent users is expected to double in the next quarter. The existing Kubernetes cluster is running at 80% of its GPU capacity during peak hours. The architect must propose a plan to scale the compute infrastructure to handle the increased load.
Which two strategies represent the most effective and scalable solutions? (Choose 2.)

• A. Vertically scale the existing nodes by adding more RAM and CPU cores to each one.
• B. Horizontally scale the cluster by adding new GPU-equipped nodes.
• C. Manually deploy all new application replicas to a single, new, very large "super-node".
• D. Configure the Kubernetes Cluster Autoscaler to automatically add new nodes to the cluster from a predefined node pool when GPU demand exceeds current capacity.
• E. Replace all existing GPUs with the next-generation model to increase the performance of each node.

An organization recently suffered a ransomware attack that encrypted several volumes on their primary ONTAP storage system, including a critical volume containing curated training data. The security team needs to implement a solution that can proactively detect and block ransomware- like file I/O patterns and automatically create a secure Snapshot copy before any damage is done.
The current ONTAP configuration is as follows:
ONTAP_Version: 9.12.1
Security_Features: SnapLock (Compliance Mode) on archive volumes
Anti-Virus_Scan: Enabled (Vscan)
Ransomware_Detection: Not configured
Which ONTAP feature should be enabled to provide this proactive, automated protection?

• A. Increase the frequency of scheduled Snapshots.
• B. Enable Autonomous Ransomware Protection (ARP).
• C. Use BlueXP backup and recovery to back up the volumes to the cloud more frequently.
• D. Enable Multi-Admin Verification (MAV) on all volumes.

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