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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Lifecycle	27%	- Model training, inference, and optimization - AI lifecycle stages: design, training, deployment, monitoring - AI governance, ethics, and compliance - Predictive vs generative AI - Data preparation and management for AI
Security, Reliability, and Operations	15%	- Monitoring, logging, and troubleshooting AI environments - Data security and access control for AI - Cost management and efficiency - High availability and data protection
Cloud and Hybrid Cloud AI Deployment	18%	- Data mobility and consistency across environments - NetApp cloud data services for AI - Hybrid and multi-cloud AI architectures - Cloud-native AI solutions and integration
AI Overview	15%	- Algorithm types: supervised, unsupervised, reinforcement learning - AI, machine learning, and deep learning concepts - AI deployment models: on-premises, cloud, edge - AI industry use cases and applications - Convergence of AI, high-performance computing, and analytics
NetApp AI Solutions and Architecture	25%	- Storage architectures for AI workloads - NetApp AI-ready infrastructure components - Scalability and performance optimization for AI - Data management and data pipeline design - ONTAP integration with AI frameworks

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Network Appliance NetApp Certified AI Expert Exam 認定 NS0-901 試験問題 (Q46-Q51):

質問 # 46
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 # 47
The company decides to establish a disaster recovery (DR) site in a secondary data center for the entire Digital Twin platform. The DR plan must protect the HPC data, the AI training data, and the central data lake.
The DR requirements are:
- RPO: 4 hours for all data.
- RTO: 24 hours for the entire platform.
- Process: The failover and failback process should be as automated as possible.
Which combination of technologies provides the most comprehensive DR solution?

A. Use BlueXP backup and recovery to back up all three storage systems (ASA, E-Series, StorageGRID) to a single cloud target.
B. Use NetApp SnapMirror for the ASA, StorageGRID replication for the data lake, and rely on tape backups for the E-Series/BeeGFS data.
C. Deploy a MetroCluster configuration spanning the primary and DR sites for all three storage systems.
D. Use NetApp SnapMirror for the ASA, StorageGRID's built-in replication for the data lake, and a host- based tool like 'rsync' for the E-Series/BeeGFS data, all orchestrated via BlueXP disaster recovery.

正解：D

質問 # 48
An architect is designing a comprehensive AI platform for a large enterprise. The platform must support the entire data lifecycle, from ingest at the edge to a central data lake, and finally to a high- performance training cluster.
The requirements are:
- Edge Ingest: Data must be collected at remote sites and efficiently replicated to the core.
- Data Lake: A central, petabyte-scale repository for unstructured data, accessible via the S3 protocol.
- Training Cluster: A high-performance compute cluster that requires low-latency, parallel file access to training datasets.
- Data Traceability: All datasets used for training must be immutably versioned.
Which combination of NetApp technologies and protocols should the architect choose to build this solution? (Select all that apply.)

A. Use NetApp ONTAP systems at the edge and NetApp SnapMirror to replicate data to the core data center.
B. Use NetApp Snapshots on the training dataset volumes to create immutable, point-in-time versions for traceability.
C. Use a NetApp E-Series system with a parallel file system (like BeeGFS) to provide high- performance, parallel file access for the training cluster.
D. Use NetApp FlexCache to tier cold data from the data lake to the public cloud.
E. Use NetApp StorageGRID to create the petabyte-scale, S3-accessible data lake at the core.
F. Use iSCSI as the primary protocol for the data lake to ensure maximum compatibility.

正解：A、B、C、E

質問 # 49
A data science team works primarily at a central data center but needs to run a short-term, burst- compute training job in the public cloud to take advantage of specialized GPUs that are not available on- premises. They need to efficiently and securely move a 20 TB dataset from their on- premises ONTAP cluster to a Cloud Volumes ONTAP instance for the duration of the job.
The data flow requirement is as follows:
Source: On-premises ONTAP cluster
Destination: Cloud Volumes ONTAP in AWS
Requirement: Efficient, secure, block-level data transfer.
Which NetApp technology is the most appropriate tool for this task?

A. NetApp FlexCache
B. NetApp SnapMirror
C. NetApp XCP
D. NetApp FabricPool

正解：B

質問 # 50
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. The PVC will remain in a "Pending" state until a PersistentVolume is manually created.
C. Trident will automatically provision a 50 Gi volume on its default backend and bind it to the PVC.
D. The PVC will fail because a 'storageClassName' is not explicitly defined.

正解：C

質問 # 51
......

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