Network Appliance NS0-901 問題集

質問 1：
An AI infrastructure team is selecting a storage protocol for a new, high-performance computing (HPC) cluster that will be used for a weather modeling AI application. The application requires extremely high-throughput, parallel access from hundreds of compute nodes to a shared dataset.
The workload characteristics are as follows:
Access_Pattern: Massively parallel reads from many clients.
File_Type: Large, shared data files.
Latency_Sensitivity: High (for metadata operations).
Primary_Requirement: Maximum aggregate throughput.
Which storage protocol is best suited for this workload?
A. An object-based storage system using S3.
B. A parallel file system like BeeGFS or Lustre.
C. A block-based protocol using iSCSI.
D. A standard file-based system using NFS.
正解：B

質問 2：
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 BlueXP Connector does not have the correct permissions to manage the SnapMirror relationship.
B. The SnapMirror relationship is broken because the destination volume at the core data center has run out of capacity.
C. The source volume at the edge location has become corrupted.
D. The network connection between the edge and the core is unreliable.
正解：B

質問 3：
The data pipeline that updates the vector database from the StorageGRID data lake fails. An MLOps engineer uses the BlueXP API to check the status of the copy and sync job responsible for moving new documents to a staging area for processing.
The API returns the following JSON response:
{
"id": "rel_5f8g7h2k",
"volumeName": "product_docs_source",
"svmName": "svm_datalake",
"relationshipStatus": "FAILED",
"s3Location": "s3://vector-staging-bucket/latest/",
"progress": 87,
"failureMessage": "Sync failed.
Target credentials invalid. Check permissions for data broker 'db-gcp-1' on target bucket 'vector- staging-bucket'."
}
Based on this output, what is the most direct cause of the pipeline failure?
A. The source StorageGRID system is offline.
B. The network connection between the data broker and the StorageGRID system is blocked by a firewall.
C. The BlueXP data broker service account or role lacks the necessary IAM permissions to write to the target S3 bucket.
D. The vector database embedding process has crashed.
正解：C

質問 4：
The pod running the vector database on the Kubernetes cluster fails to start. An MLOps engineer runs 'kubectl describe pod vector-db-pod-0' and sees the following event message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 30s default-scheduler 0/8 nodes are available: 8 node(s) did not match pod anti-affinity rules.
The pod's manifest contains the following 'affinity' definition:
affinity:
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- labelSelector:
matchExpressions:
- key: app
operator: In
values:
- vector-db
topologyKey: "kubernetes.io/hostname"
What is the most likely reason the pod cannot be scheduled?
A. The PersistentVolumeClaim for the pod is in a 'Pending' state.
B. The 'topologyKey' is invalid; it should be 'failure-domain.beta.kubernetes.io/zone'.
C. The 'vector-db-pod-0' is requesting more GPU resources than are available on any node.
D. The pod's anti-affinity rule prevents it from being scheduled on any node that is already running another pod with the label 'app=vector-db'.
正解：D

質問 5：
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 exclusively uses object storage, which simplifies access for data scientists using S3-native tools.
B. It is a reference architecture that is pre-validated by NetApp and NVIDIA to eliminate design complexity and ensure predictable performance for AI workloads.
C. It prioritizes CPU performance over GPU performance for traditional machine learning algorithms.
D. It is designed for small-scale, departmental AI projects and cannot be scaled.
正解：B