NS0-901問題集、Network Appliance実際の試験問題

質問 1

Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)

A. TensorFlow Extended

B. RunAI

C. KubeFlow

D. Google VertexAI

正解: B,C

質問 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. A hybrid model using a public cloud for training and on-premises for inference.

B. An on-premises private cloud for training combined with edge deployments in each hospital for inference.

C. A multi-cloud strategy using different providers for training and inference to avoid vendor lock-in.

D. A centralized public cloud deployment in North America for maximum scalability.

正解: B

質問 3

An AI operations team is troubleshooting why their RAG-based chatbot is providing outdated information.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. Modify the NFS export policy on the 'vol_kb' volume on the on-premises ONTAP cluster to grant access to the BlueXP Connector.

C. Re-run the vector database embedding job.

D. Fine-tune the LLM with the latest data instead of using the RAG system.

正解: B

質問 4

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. 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.

B. Deploy a single, global NetApp StorageGRID across all three locations to act as a unified data plane.

C. 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.

D. Use Cloud Volumes ONTAP at the edge, NetApp StorageGRID at the core, and on-premises ONTAP for cloud archive.

正解: A

質問 5

A research lab uses a fleet of autonomous drones to collect high-resolution aerial imagery for agricultural analysis. The drones land at a remote edge location and offload their data. The AI models for image analysis are trained at a central data center. The team is using NetApp SnapMirror to replicate the data from the edge to the core. However, the data scientists are complaining that the datasets arriving at the data center are often incomplete or corrupted.
An administrator reviews the SnapMirror configuration and status via the BlueXP API:
{
"source": { "workingEnvironmentId": "OnPrem-Edge-Filer-1", "volumeName": "drone_data_raw" },
"destination": { "workingEnvironmentId": "Core-Datacenter-A800", "volumeName":
"drone_data_replicated" },
"mirrorState": "broken-off",
"relationshipStatus": "idle",
"unhealthyReason": "Transfer failed. Destination volume is out of space.", "lastTransferInfo": {
"transferError": "No space left on device"
}
}
What is the direct cause of the incomplete datasets at the data center?

A. The SnapMirror relationship is broken because the destination volume at the core data center has run out of capacity.

B. The network connection between the edge and the core is unreliable.

C. The source volume at the edge location has become corrupted.

D. The BlueXP Connector does not have the correct permissions to manage the SnapMirror relationship.

正解: A

質問 6

A development team is building a generative AI application that must answer questions based on a constantly changing internal knowledge base of company documents. They want to provide the model with up-to-date information without altering its core weights and capabilities.
Which approach is most suitable for this requirement?

A. Retrieval-Augmented Generation (RAG), which fetches relevant information from the documents at query time to inform the model's response.

B. Fine-tuning, which retrains the model's last few layers on the new documents.

C. Unsupervised learning, which clusters the documents into topics for the model to browse.

D. Predictive AI, which analyzes the documents to forecast future questions.

正解: A

質問 7

What is the primary architectural benefit of using technologies like RDMA (Remote Direct Memory Access) and GPUDirect Storage in a high-performance AI training cluster?

A. They allow GPUs to communicate directly with each other and with NVMe storage, bypassing the host CPU and system memory, which significantly reduces I/O latency and CPU overhead.

B. They reduce the power consumption of the storage array by offloading checksum calculations.

C. They enable the use of lower-cost Ethernet switches in place of InfiniBand fabrics without any performance degradation.

D. They automatically encrypt data in-flight between the GPU servers and the storage system.

正解: A

質問 8

Given the company's goal of combining physics-based simulations with AI-driven analytics on a shared data foundation, which industry trend does this project best represent?

A. The replacement of all physical testing with digital simulations.

B. The convergence of AI, High-Performance Computing (HPC), and analytics on a unified data infrastructure.

C. The exclusive use of public cloud resources for all computational tasks.

D. The separation of AI and HPC into dedicated, air-gapped environments.

正解: B

質問 9

A data scientist needs to test a new data normalization technique. To do this, they require an isolated, writable copy of a 50 TB curated simulation dataset that resides on the NetApp ASA system. The operation must be completed as quickly as possible and consume minimal additional storage space. Which NetApp technology is the most appropriate solution for this requirement?

A. NetApp FabricPool

B. NetApp FlexClone

C. NetApp SnapMirror

D. NetApp XCP

正解: B

Network Appliance NetApp Certified AI Expert - NS0-901 模擬練習