Network Appliance NS0-901 : NetApp Certified AI Expert Exam

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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

1. The company's compliance officer mandates that all raw simulation data in the StorageGRID data lake must be protected from modification or deletion for a period of seven years. Which two StorageGRID features should be implemented to meet this strict, long-term data immutability requirement? (Choose 2.)

A) Enable Autonomous Ransomware Protection (ARP) on the StorageGRID system.
B) Create a new S3 bucket with Object Lock enabled in Compliance mode.
C) Configure an Information Lifecycle Management (ILM) rule that applies to the bucket and sets a retention period of seven years.
D) Use NetApp SnapMirror to replicate the data to a second StorageGRID system.
E) Encrypt all objects in the bucket using server-side encryption (SSE).

2. 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) Rely on hostPath volumes for all storage to ensure the highest performance.
B) Create a single, large NFS volume and mount it to all pods using a static PersistentVolume.
C) Use the NetApp DataOps Toolkit for all storage provisioning, bypassing Trident and Kubernetes PVCs.
D) Configure multiple Trident backends (e.g., 'ontap-nas' for standard volumes, 'ontap-nas-flexgroup' for parallel access) and corresponding StorageClasses.
E) Use the NetApp DataOps Toolkit for Python within the Kubeflow pipeline components to dynamically create and manage Trident PVCs for each stage.

3. The "Advisor Assistant" application, running as a pod in Kubernetes, suddenly cannot access its data on the AFF A-Series. The application logs show "connection timed out" errors. The network team provides a firewall log snippet for the traffic between the application pod and the storage system's NFS data LIF.
TIME | SRC_IP | DST_IP | PROTO | DST_PORT | ACTION
-|--||-|-|-
2025-07-11T16:01:10Z | 10.20.5.101 (Pod) | 10.20.10.55 (LIF) | TCP | 111 | BLOCKED 2025-07-
11T16:01:12Z | 10.20.5.101 (Pod) | 10.20.10.55 (LIF) | TCP | 2049 | BLOCKED What is the most likely cause of the connectivity failure?

A) The ONTAP system's data LIF is offline.
B) The AFF A-Series does not support the NFS protocol.
C) A network firewall between the pod's subnet and the storage subnet is blocking the required NFS ports.
D) The application pod has incorrect mount options configured.

4. An MLOps team uses a variety of platforms to manage their AI workloads. They need to understand the primary function of each tool within their ecosystem. Which statement best describes the role of an MLOps/LLMOps platform like Kubeflow or Run:AI?

A) They are orchestration and management platforms that automate and streamline the entire AI/ML lifecycle, from data preparation and model training to deployment and monitoring.
B) They are integrated development environments (IDEs) used exclusively for writing Python code.
C) They are networking protocols designed to accelerate data transfer between GPUs.
D) They are specialized storage systems designed to hold large datasets for training.

5. The architect is designing the complete, automated data pipeline from the on-premises data center to the Azure cloud for this medical imaging project. The design must prioritize security, efficiency, and reproducibility.
Which sequence of actions provides the most robust and automated solution?

A) Use SnapMirror to replicate the raw, sensitive data to Azure first, then run BlueXP classification in the cloud to find PII.
B) Manually copy raw data to Azure, run a cloud-based script to anonymize it, then start training.
C) Use FabricPool to automatically tier all raw data to Azure Blob storage, then mount the blob container to the training VMs.
D) Use BlueXP classification to scan the on-prem data; use a DataOps Toolkit script to trigger a FlexClone of the source volume for anonymization; use SnapMirror to replicate the anonymized clone to Cloud Volumes ONTAP; then begin training in Azure.

Solutions: