最新なNetwork Appliance NS0-901問題集（106題）、真実試験の問題を全部にカバー！

質問 1：
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. Implement NetApp FabricPool to automatically tier cold data blocks from the AFF systems to StorageGRID.
C. Use BlueXP backup and recovery to create backups on StorageGRID, then delete the original volumes from the AFF systems.
D. Use NetApp XCP to periodically move cold data from the AFF systems to StorageGRID.
E. Train data scientists to use NetApp Snapshots to create point-in-time, read-only versions of their data volumes.
F. Use NetApp SnapMirror to replicate volumes from the AFF systems to StorageGRID for archival.
正解：B,E

質問 2：
Which of the following applications use AI in the healthcare industry? (Choose two)
A. Predicting patient outcomes
B. Managing hospital supply chains
C. Diagnosing diseases
D. Automating financial reporting
正解：A,C

質問 3：
What is the primary architectural advantage of using a NetApp AIPod with NVIDIA DGX servers for the AI training cluster, as described in the scenario?
A. It prioritizes CPU performance over GPU performance for traditional machine learning algorithms.
B. It exclusively uses object storage, which simplifies access for data scientists using S3-native tools.
C. It is a reference architecture that is pre-validated by NetApp and NVIDIA to eliminate design complexity and ensure predictable performance for AI workloads.
D. It is designed for small-scale, departmental AI projects and cannot be scaled.
正解：C

質問 4：
An organization is planning to deploy a large AI infrastructure but wants to avoid a large, upfront capital expenditure. They prefer an operational expenditure (OpEx) model where they pay for storage and compute resources as they are consumed. They also need the flexibility to scale resources up or down based on project demands.
Which NetApp consumption model is specifically designed to meet these financial and operational requirements?
A. NetApp Cloud Volumes ONTAP PAYGO licensing only.
B. A standard capital purchase of AFF and ASA systems.
C. A perpetual licensing model for all ONTAP software features.
D. NetApp Keystone, which provides a subscription-based, pay-as-you-go service for on-premises and cloud storage.
正解：D

質問 5：
An architect is designing a data pipeline for a predictive AI model that will forecast retail sales.
The pipeline must be robust, version-controlled, and efficient.
The proposed data flow is as follows:
1. Ingest: Raw sales data is copied daily from multiple point-of-sale (POS) systems to a central staging area on an on-premises ONTAP cluster.
2. Prepare: The raw data is messy. A data engineering team needs a clean, isolated, and writable copy of the latest daily data to perform cleansing and feature engineering tasks without impacting the original raw data.
3. Train: Once prepared, the cleansed dataset is used to retrain the predictive model on a GPU cluster.
This step must be repeatable with the exact same dataset for compliance.
4. Deploy: The newly trained model is pushed to production inference servers.
Which combination of NetApp technologies best supports this entire predictive AI lifecycle?
(Select all
that apply.)
A. Use NetApp StorageGRID as the primary storage for the high-performance training stage.
B. Use NetApp FlexClone to create an instantaneous, space-efficient, writable copy of the daily raw data for the data preparation stage.
C. Use NetApp XCP to efficiently aggregate the raw sales data from POS systems into the central staging area.
D. Use BlueXP backup and recovery to perform the initial data ingest from the POS systems.
E. Use NetApp Snapshots on the prepared dataset volume just before training to create an immutable, point-in-time version for compliance and reproducibility.
F. Use a RAG architecture for the sales forecasting model.
正解：B,C,E

質問 6：
The firm has acquired a competitor, and the volume of proprietary documents for the "Advisor Assistant" is expected to triple, exceeding the capacity of the current StorageGRID data lake. The architect needs to expand the data lake's capacity non-disruptively.
The current StorageGRID status is:
System_Health: Nominal
Node_Count: 6 (3 Storage Nodes, 3 Admin/Gateway Nodes)
Usable_Capacity: 1.5 PB
Used_Capacity: 1.4 PB (93%)
What is the standard procedure for scaling the capacity of the on-premises NetApp StorageGRID system?
A. Provision a new, separate StorageGRID system and use BlueXP copy and sync to move the data.
B. Replace the disks in the existing storage nodes with higher-capacity drives.
C. Add new storage nodes to the existing StorageGRID system and execute an expansion procedure.
D. Use FabricPool to tier the excess data from StorageGRID to a public cloud provider.
正解：C