Network Appliance NS0-901

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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

1. The data anonymization job, running in a Kubernetes pod, fails. The pod logs show a "Permission Denied" error when trying to access the source volume on the on-premises ASA. An administrator checks the export policy rule for the volume.
The active rule is as follows:
Rule_Index: 1
Client_Match: 10.50.0.0/16
Protocols: nfs4
Read_Only_Access: sys
Read_Write_Access: -
Superuser_Access: none
The Kubernetes pod that failed has an IP address of '10.60.5.10'.
What is the cause of the "Permission Denied" error?

A) The 'Superuser_Access' setting is too restrictive.
B) The export policy only allows access via the NFSv4 protocol.
C) The export policy does not grant read-write access, which is required by the anonymization job.
D) The pod's IP address ('10.60.5.10') is not within the allowed client match range ('10.50.0.0/16').

2. An architect is designing the storage and network infrastructure for a new, large-scale AI cluster dedicated to training foundational models. The primary design goal is to achieve the highest possible data throughput and the lowest latency to ensure multi-million dollar GPU resources are never idle. Which two technologies are essential to include in the design to achieve this goal?
(Choose 2.)

A) NetApp StorageGRID as the primary storage for the training datasets.
B) A 10GbE Ethernet network for all data traffic.
C) GPUDirect Storage support on the storage system.
D) A tiered storage architecture using NetApp FabricPool.
E) An InfiniBand or RoCE-capable Ethernet network fabric.

3. An architect is designing a fully automated, end-to-end MLOps pipeline on Kubernetes for a computer vision use case. The pipeline must handle everything from data versioning to model deployment.
The required pipeline stages are:
1. Data Versioning: Create a new, immutable version of the master dataset for the pipeline run.
2. Data Preparation: Launch a pod to run a preprocessing script on the versioned data.
3. Model Training: Launch a distributed training job that reads the prepared data from a highperformance volume.
4. Model Deployment: Push the trained model to a production inference service.
Which combination of NetApp and Kubernetes technologies provides the most effective and automated solution for this entire pipeline?

A) Use the NetApp DataOps Toolkit to create a Snapshot of the source data volume (for versioning), then create a FlexClone PVC from the snapshot for the preparation stage, and finally create a FlexGroup PVC for the training stage.
B) Use NetApp XCP to copy the data for each stage and configure static PersistentVolumes for each pod.
C) Use a single, large ReadWriteMany PVC for all stages to simplify the pipeline configuration.
D) Use NetApp SnapMirror for data versioning and manually create hostPath volumes for each pipeline stage.
E) Manually create a NetApp Snapshot via System Manager before each pipeline run, and use the NetApp DataOps Toolkit only for the training stage.

4. 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

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

Solutions: