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

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Network Appliance NS0-901 試験シラバストピック:

Network Appliance NetApp Certified AI Expert 認定 NS0-901 試験問題：

1. A financial services company is required by regulators to be able to trace any version of their deployed fraud detection model back to the exact dataset and source code commit used to train it.
The current MLOps workflow is as follows:
Code_Repository: Git (commit hash: a1b2c3d4)
Dataset_Location: /vol/prod_data/fraud_dataset_v3
Storage_System: NetApp ONTAP 9
Model_Output: /vol/models/fraud_model_v3.2
Which NetApp technology should be used to create an immutable, point-in-time, and space- efficient copy of the dataset that can be linked to the specific code commit and model version?

A) NetApp FlexClone
B) NetApp FabricPool
C) NetApp SnapMirror
D) NetApp Snapshots

2. An AI operations team is troubleshooting why their RAG-based chatbot is providing outdated information.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) Re-run the vector database embedding job.
D) Modify the NFS export policy on the 'vol_kb' volume on the on-premises ONTAP cluster to grant access to the BlueXP Connector.

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 private vector database hosted on an on-premises NetApp ONTAP system to store embeddings of the sensitive corporate data.
B) A BlueXP Connector deployed in a public subnet with a public IP address to allow access to the LLM.
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 direct VPN connection from each data scientist's laptop to the public LLM provider.
E) A policy in BlueXP classification to copy all sensitive data to a public cloud bucket for easier access.

4. A data scientist is using the NetApp DataOps Toolkit for Python to automate the creation of a new, writable volume for an experiment. The script is intended to clone an existing dataset volume. When the script is executed, it fails with an error.
The relevant portion of the Python script is:
from netapp_dataops.k8s import clone_pvc
clone_pvc(
source_pvc_name="dataset-v1-pvc",
new_pvc_name="experiment-clone-pvc",
namespace="ds-team-1"
)
The script produces the following error in the terminal:
'Error: Failed to clone PVC. Source PVC 'dataset-v1-pvc' not found in namespace 'ds-team-1'.' What is the most likely cause of this error?

A) The Python script is missing the necessary import statement for the toolkit.
B) The NetApp DataOps Toolkit does not support cloning volumes.
C) The Kubernetes cluster does not have NetApp Trident installed.
D) The source PersistentVolumeClaim (PVC) named 'dataset-v1-pvc' does not exist or is in a different namespace.

5. An organization is developing a new AI-powered application. The initial phase involves feeding a curated 50 TB dataset of labeled images into a complex neural network, allowing the model to learn and adjust its internal parameters over millions of iterations. The second phase involves deploying this finalized model to a web service where it will process single, user-uploaded images and return a classification in real-time.
Which statement accurately describes these two phases?

A) Both Phase 1 and Phase 2 are examples of training.
B) Both Phase 1 and Phase 2 are examples of inferencing.
C) Phase 1 is inferencing, and Phase 2 is training.
D) Phase 1 is training, and Phase 2 is inferencing.

質問と回答：