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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

1. 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 XCP
C) NetApp FlexClone
D) NetApp SnapMirror

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

4. 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) A standard file-based system using NFS.
B) A parallel file system like BeeGFS or Lustre.
C) A block-based protocol using iSCSI.
D) An object-based storage system using S3.

5. An MLOps engineer is troubleshooting a failed Kubeflow pipeline step. The step was designed to create a clone of a dataset for a training job using the NetApp DataOps Toolkit. The pod logs for the failed pipeline step show the following:
Traceback (most recent call last):
File "create_clone.py", line 15, in <module>
clone_pvc(source_pvc_name="training-data-v2", new_pvc_name="train-job-34a-data") NameError: name 'clone_pvc' is not defined The engineer reviews the Python script for the pipeline step:
# create_clone.py
import os
from netapp_dataops.k8s import create_pvc
# Other code
print("Cloning source dataset for training run...")
clone_pvc(
source_pvc_name="training-data-v2",
new_pvc_name="train-job-34a-data"
)
print("Clone created successfully.")
What is the cause of the error?

A) The NetApp DataOps Toolkit is not installed in the container image used for this pipeline step.
B) The Python script is attempting to use the 'clone_pvc' function, but it was not imported from the
'netapp_dataops.k8s' library.
C) The source PVC 'training-data-v2' does not exist.
D) The Kubernetes cluster is not running NetApp Trident.

Solutions: