An architect is designing a fully automated, end-to-end MLOps pipeline on Kubernetes for a computer vision use case. The pipeline must handle everything from data versioning to model deployment.
The required pipeline stages are:
1. Data Versioning: Create a new, immutable version of the master dataset for the pipeline run.
2. Data Preparation: Launch a pod to run a preprocessing script on the versioned data.
3. Model Training: Launch a distributed training job that reads the prepared data from a highperformance volume.
4. Model Deployment: Push the trained model to a production inference service.
Which combination of NetApp and Kubernetes technologies provides the most effective and automated solution for this entire pipeline?

• A. Use NetApp SnapMirror for data versioning and manually create hostPath volumes for each pipeline stage.
• B. Manually create a NetApp Snapshot via System Manager before each pipeline run, and use the NetApp DataOps Toolkit only for the training stage.
• C. Use a single, large ReadWriteMany PVC for all stages to simplify the pipeline configuration.
• D. Use the NetApp DataOps Toolkit to create a Snapshot of the source data volume (for versioning), then create a FlexClone PVC from the snapshot for the preparation stage, and finally create a FlexGroup PVC for the training stage.
• E. Use NetApp XCP to copy the data for each stage and configure static PersistentVolumes for each pod.

Which storage protocols are commonly used for handling large-scale AI data? (Choose two)

• A. File-based systems
• B. Object-based storage
• C. POSIX-based file systems
• D. Parallel file systems

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

An AI operations team is troubleshooting why their RAG-based chatbot is providing outdated information. They have confirmed that the vector database embedding process is functioning correctly, but suspect an issue with the initial data synchronization that moves the knowledge base from an on- premises ONTAP file share to a cloud staging bucket.
They inspect the relevant BlueXP copy and sync job and find the following details:
Service: BlueXP copy and sync
Relationship_Name: KB_Sync_to_Vector_Staging
Source: nfs://ontap-cluster-1/vol_kb/docs
Destination: s3://vector-staging-bucket-89a3/latest/
Last_Sync_Status: FAILED
Last_Sync_Time: 2025-07-11T02:00:15Z
Error_Message: "Authentication error:
Unable to access source.
Check export policy on 'vol_kb'."
Based on this information, what is the most direct solution to fix the data pipeline?

• A. Check the IAM permissions for the role associated with the S3 bucket.
• B. Fine-tune the LLM with the latest data instead of using the RAG system.
• C. Modify the NFS export policy on the 'vol_kb' volume on the on-premises ONTAP cluster to grant access to the BlueXP Connector.
• D. Re-run the vector database embedding job.