Network Appliance NS0-901 Questions & Answers - in .pdf
- Vendor: Network Appliance
- Exam Code: NS0-901
- Exam Name: NetApp Certified AI Expert Exam
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- Vendor: Network Appliance
- Exam Code: NS0-901
- Exam Name: NetApp Certified AI Expert Exam
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Network Appliance NetApp Certified AI Expert Sample Questions:
1. Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)
A) Google VertexAI
B) RunAI
C) TensorFlow Extended
D) KubeFlow
2. A junior administrator is attempting to delete an old, unused Snapshot copy from a production volume to reclaim space but receives an error. The administrator is certain they are using the correct command and have the appropriate 'vsadmin' role.
The command and error are shown below:
cluster-1::> volume snapshot delete -vserver svm_prod -volume app_data -snapshot nightly.2025- 06-15_0015 Error: command failed: This operation is not permitted. Deletion of Snapshot copy "nightly.2025-
06-15_0015" on volume "app_data" in Vserver "svm_prod" requires approval.
Use the "security multi-admin-verify approval show" command to view pending approvals.
What is the most likely reason the administrator cannot delete the Snapshot?
A) The volume is protected by SnapLock, and the Snapshot has not reached its expiry date.
B) The Snapshot is locked by a SnapMirror relationship.
C) Multi-Admin Verification (MAV) is enabled, and the destructive operation requires approval from another designated administrator.
D) The 'vsadmin' role does not have permission to delete Snapshots.
3. The architect needs to design an efficient data flow to move curated training sets from the central StorageGRID data lake to the high-performance NetApp ASA system used by the AI cluster. The process must be manageable from a single interface and should be automatable. Which two NetApp technologies should be used to implement this data pipeline stage? (Choose 2.)
A) NetApp BlueXP copy and sync service to create and manage the data synchronization relationship between the S3 source and the NFS/CIFS destination.
B) NetApp FabricPool to automatically tier the data from StorageGRID to the ASA.
C) A custom Python script using the NetApp DataOps Toolkit to trigger the data movement.
D) A manual copy process using an S3 client on a bastion host.
E) NetApp SnapMirror to replicate data from the StorageGRID object store to the ASA's file system.
4. A training job on one of the NVIDIA DGX servers is running slowly. A performance engineer runs the 'dstat' command on the server and captures the following output during the job execution.
-total-cpu-usage- -dsk/total- -net/total- paging-- system-- usr sys idl wai hiq siq| read writ| recv send| in out | int csw 15 5 70 10 0 0| 1.2G 15.0M| 1.2G 12.0M| 0 0 | 15k 35k
14 6 69 11 0 0| 1.2G 14.3M| 1.2G 11.8M| 0 0 | 14k 33k
16 5 68 11 0 0| 1.2G 16.1M| 1.2G 13.1M| 0 0 | 16k 36k
The server is connected to the NetApp ASA via a 100GbE (12.5 GB/s) network.
What is the most likely performance bottleneck based on this data?
A) The server's network interface is saturated.
B) The application is experiencing excessive memory paging.
C) The storage system (ASA) is unable to provide sufficient read throughput.
D) The server's CPU is overloaded.
5. An architect is designing a scalable, automated MLOps platform using Kubeflow on a Kubernetes cluster. The platform must support the entire AI lifecycle for multiple teams, with different storage requirements at each stage.
The key requirements are:
- Data Ingestion: A pipeline step needs a shared, read-write volume accessible by multiple pods to stage raw data.
- Experimentation: Data scientists need individual, isolated volumes for their Jupyter notebooks.
- Training: Distributed training jobs require a high-performance, parallel-access filesystem for reading training data.
- Automation: All storage must be provisioned automatically via Kubeflow pipeline definitions without manual intervention.
Which combination of technologies and configurations would create the most effective solution?
A) Use the NetApp DataOps Toolkit for all storage provisioning, bypassing Trident and Kubernetes PVCs.
B) Configure multiple Trident backends (e.g., 'ontap-nas' for standard volumes, 'ontap-nas-flexgroup' for parallel access) and corresponding StorageClasses.
C) Create a single, large NFS volume and mount it to all pods using a static PersistentVolume.
D) Rely on hostPath volumes for all storage to ensure the highest performance.
E) Use the NetApp DataOps Toolkit for Python within the Kubeflow pipeline components to dynamically create and manage Trident PVCs for each stage.
Solutions:
| Question # 1 Answer: B,D | Question # 2 Answer: C | Question # 3 Answer: A,C | Question # 4 Answer: A | Question # 5 Answer: B,E |
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