Network Appliance NetApp Certified AI Expert 問題集 : NS0-901

An internal audit requires the firm to prove the exact state of the financial product documentation that was used by the chatbot to answer a specific query from last Tuesday. The query log shows the request was processed at '2025-07-11T14:30:00Z'. The data pipeline that updates the vector database runs daily at midnight.
Which NetApp technology allows the team to instantly access an immutable, point-in-time version of the vector database volume as it existed on that specific day?

• A. NetApp FlexClone
• B. NetApp FabricPool
• C. NetApp SnapMirror
• D. NetApp Snapshots

A data science team works primarily at a central data center but needs to run a short-term, burst- compute training job in the public cloud to take advantage of specialized GPUs that are not available on- premises. They need to efficiently and securely move a 20 TB dataset from their on- premises ONTAP cluster to a Cloud Volumes ONTAP instance for the duration of the job.
The data flow requirement is as follows:
Source: On-premises ONTAP cluster
Destination: Cloud Volumes ONTAP in AWS
Requirement: Efficient, secure, block-level data transfer.
Which NetApp technology is the most appropriate tool for this task?

• A. NetApp XCP
• B. NetApp FabricPool
• C. NetApp SnapMirror
• D. NetApp FlexCache

To comply with the security mandate, the architect must design a process to prevent client PII from the portfolio database from ever being included in the context sent to the LLM. Which two actions are required to build a robust and automated solution for this? (Choose 2.)

• A. Move the entire client portfolio database to an encrypted, air-gapped system.
• B. Implement a data preparation step in the RAG pipeline that programmatically masks or excludes any data fields tagged as PII before the context is constructed.
• C. Use BlueXP classification to perform an initial scan of the portfolio database to identify and tag all columns containing PII.
• D. Fine-tune the LLM on a sanitized dataset to teach it to ignore PII.
• E. Rely on a disclaimer in the chatbot UI telling advisors not to enter queries related to specific clients.

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. Increase the storage quota for the 'jupyter-pvc-user01' PVC.
• B. 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.
• C. Manually create the PersistentVolumeClaim using 'kubectl'.
• D. Restart the BlueXP Connector to refresh its permissions.

Given the firm's requirements for using a private, constantly updated knowledge base and the strict mandate for data traceability, which AI architecture is the most appropriate foundation for the "Advisor Assistant" chatbot?

• A. A fine-tuning architecture where a base LLM is continuously retrained on the entire document repository.
• B. A predictive AI model trained to forecast market trends.
• C. A standalone LLM deployed in an air-gapped environment with no access to the document repository.
• D. A Retrieval-Augmented Generation (RAG) architecture that retrieves relevant context from a local vector database to enrich prompts sent to the LLM.