最新的 NetApp Certified AI Expert NS0-901 免費考試真題:

1. An AI architect is designing a solution for a legal firm. The primary goal is to allow lawyers to ask natural language questions about case law stored in a private, 50 TB document repository.
The key project constraints are as follows:
Project_Goal: Answer questions using proprietary, real-time legal documents.
Constraint_1: Must not alter the foundational LLM's weights due to compliance.
Constraint_2: Case law database is updated daily with new rulings.
Constraint_3: All generated answers must be traceable to a source document.
Which technology should the architect choose as the core of this solution?

A) A predictive AI model to classify legal documents.
B) A new LLM trained from scratch on the legal documents.
C) A Retrieval-Augmented Generation (RAG) architecture.
D) A fine-tuning pipeline to update the LLM daily.

2. An architect is designing an AI solution for a European hospital chain to analyze patient diagnostic scans. The project is subject to strict GDPR regulations, which mandate that patient data cannot leave the sovereign territory. The application also requires near-instantaneous results for physicians reviewing the scans in the hospital.
Which deployment model best satisfies these security and performance requirements?

A) An on-premises private cloud for training combined with edge deployments in each hospital for inference.
B) A multi-cloud strategy using different providers for training and inference to avoid vendor lock-in.
C) A centralized public cloud deployment in North America for maximum scalability.
D) A hybrid model using a public cloud for training and on-premises for inference.

3. An organization wants to provide its data science team with a secure, on-demand method for using a powerful generative AI model with their private, sensitive corporate data. The solution must ensure that the private data is never exposed to the public internet or the public LLM API endpoint.
The architect is designing a solution using BlueXP.
Which two components are essential for building this secure solution? (Choose 2.)

A) A BlueXP Connector deployed in a public subnet with a public IP address to allow access to the LLM.
B) A private vector database hosted on an on-premises NetApp ONTAP system to store embeddings of the sensitive corporate data.
C) The BlueXP GenAI Toolkit, which acts as a proxy to intercept user prompts and enrich them with data from a local vector database.
D) A policy in BlueXP classification to copy all sensitive data to a public cloud bucket for easier access.
E) A direct VPN connection from each data scientist's laptop to the public LLM provider.

4. An MLOps engineer is troubleshooting a failed Kubeflow pipeline step. The step was designed to create a clone of a dataset for a training job using the NetApp DataOps Toolkit. The pod logs for the failed pipeline step show the following:
Traceback (most recent call last):
File "create_clone.py", line 15, in <module>
clone_pvc(source_pvc_name="training-data-v2", new_pvc_name="train-job-34a-data") NameError: name 'clone_pvc' is not defined The engineer reviews the Python script for the pipeline step:
# create_clone.py
import os
from netapp_dataops.k8s import create_pvc
# Other code
print("Cloning source dataset for training run...")
clone_pvc(
source_pvc_name="training-data-v2",
new_pvc_name="train-job-34a-data"
)
print("Clone created successfully.")
What is the cause of the error?

A) The Python script is attempting to use the 'clone_pvc' function, but it was not imported from the
'netapp_dataops.k8s' library.
B) The Kubernetes cluster is not running NetApp Trident.
C) The NetApp DataOps Toolkit is not installed in the container image used for this pipeline step.
D) The source PVC 'training-data-v2' does not exist.

5. An AI infrastructure architect is tasked with designing a solution to address two critical challenges in a large, multi-petabyte AI environment:
1. Cost: A significant portion of the data on the high-performance all-flash storage is inactive but must remain online. The cost of storing this cold data on the performance tier is prohibitive.
2. Traceability: Data scientists need a simple, space-efficient way to version their datasets at key points in their workflow to ensure reproducibility.
The environment consists of NetApp AFF A-Series and NetApp StorageGRID systems.
Which combination of NetApp technologies should the architect implement to solve both challenges simultaneously? (Select all that apply.)

A) Implement NetApp FlexClone to create full, writable copies of datasets for each experiment.
B) Use NetApp XCP to periodically move cold data from the AFF systems to StorageGRID.
C) Implement NetApp FabricPool to automatically tier cold data blocks from the AFF systems to StorageGRID.
D) Train data scientists to use NetApp Snapshots to create point-in-time, read-only versions of their data volumes.
E) Use NetApp SnapMirror to replicate volumes from the AFF systems to StorageGRID for archival.
F) Use BlueXP backup and recovery to create backups on StorageGRID, then delete the original volumes from the AFF systems.

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