2026にNS0-901を受験する方へ、Fast2testは無料サンプルから購入後の更新まで一貫した学習環境を提供しています。Network Appliance NetApp Certified AI Expertの106問の練習問題を活用して、計画立案から直前確認までを一つの流れで進められます。

NS0-901試験対策に選べる3つの学習形式

• PDF版：印刷して持ち運べる形式で、専門家が作成した内容をすぐにダウンロードして確認できます。学習場所を選ばず、365日間の無料更新と無料PDFサンプルに対応しています。
• Desktop Test Engine：インストールして使用するソフトウェアで、実際の試験環境を再現した2つの練習モードを利用できます。オフライン学習に対応し、Windowsで動作します。
• Online Test Engine：ブラウザーからすぐにアクセスでき、学習履歴と成績を確認できます。Windows、Mac、Android、iOSで利用できます。

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Network Appliance NetApp Certified AI Expertの試験情報

Network Appliance NS0-901 試験概要:

NS0-901試験の出題範囲

Network Appliance NS0-901 試験シラバストピック:

NS0-901試験の疑問を解消するFAQ

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

問題 #1

Which of the following platforms provides tools for model training and deployment specifically for AI workloads?

A. Google VertexAI
B. All of the above
C. Domino Data Labs
D. RunAI

問題 #2

A data scientist needs to launch a Jupyter notebook as a pod in a Kubernetes cluster. The pod requires a 50 Gi persistent volume for storing datasets and notebooks. The cluster administrator has configured a default Trident StorageClass for general-purpose use. The data scientist has the following PersistentVolumeClaim (PVC) manifest:
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: jupyter-pvc
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 50Gi
When this PVC is applied to the cluster, what will be the result?

A. Trident will create a 1 Gi volume, as this is the default size for all PVCs.
B. Trident will automatically provision a 50 Gi volume on its default backend and bind it to the PVC.
C. The PVC will remain in a "Pending" state until a PersistentVolume is manually created.
D. The PVC will fail because a 'storageClassName' is not explicitly defined.

問題 #3

An architect is designing a global infrastructure for a company that develops AI for autonomous vehicles.
The design must accommodate three distinct locations and functions:
1. Edge (Test Tracks): Fleets of test cars generate 100s of TBs of sensor data per day. This data must be ingested locally with high performance.
2. Core (Primary Data Center): The raw data from all edge sites must be aggregated here. This location houses the primary data lake and the main GPU cluster for large-scale model training.
3. Cloud (Public Cloud Provider): Data scientists want to use cloud-native tools for experimental data processing and model development. They also need a cost-effective location for long-term archiving of raw data.
Which combination of deployment locations and NetApp technologies creates the most logical and efficient end-to-end solution?

A. Deploy a single, global NetApp StorageGRID across all three locations to act as a unified data plane.
B. Use NetApp ONTAP systems at the edge and core, and Cloud Volumes ONTAP in the public cloud.
Use SnapMirror to replicate data from edge to core, and FabricPool to tier data from the core to the cloud.
C. Use Cloud Volumes ONTAP at the edge, NetApp StorageGRID at the core, and on-premises ONTAP for cloud archive.
D. Use NetApp E-Series at the edge, NetApp ASA at the core, and NetApp StorageGRID in the cloud.
Use SnapMirror to move data between all three tiers.

問題 #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 NetApp DataOps Toolkit is not installed in the container image used for this pipeline step.
C. The source PVC 'training-data-v2' does not exist.
D. The Kubernetes cluster is not running NetApp Trident.

問題 #5

Which of the following are typical requirements for inferencing in AI models? (Choose two)

A. Pre-trained models
B. Extensive training data
C. Loading the model into memory
D. Cloud-based inference only

解説：