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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Topic 1: AI Infrastructure and NetApp Solutions	- Converged workloads 1. Shared infrastructure design considerations 2. AI, HPC, and analytics convergence - AI-ready data infrastructure 1. Data pipeline optimization for AI/ML workloads 2. High-performance storage for AI workloads
Topic 2: Industry Use Cases	- AI applications across industries 1. Digital twins 2. Healthcare AI 3. Autonomous systems and agents
Topic 3: AI Lifecycle and Deployment	- Operational challenges 1. Scalability and performance optimization 2. Data governance and management - End-to-end AI lifecycle 1. Data ingestion and preparation 2. Model deployment and monitoring 3. Model training and validation
Topic 4: AI and Machine Learning Fundamentals	- Algorithm types 1. Supervised learning 2. Reinforcement learning 3. Unsupervised learning - AI, ML, DL concepts 1. Differences between AI, machine learning, and deep learning 2. Training, inference, and prediction workflows

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q34-Q39):

NEW QUESTION # 34
A robotics company is developing a control system for an autonomous warehouse drone. The drone must learn to navigate complex environments to pick up packages. The development team has created a physics-based simulation where the drone can attempt the task millions of times.
The drone receives a positive reward for successfully retrieving a package and a negative penalty for collisions. Which type of machine learning algorithm is being used in this scenario?

A. Reinforcement learning
B. Supervised learning
C. Unsupervised learning
D. Generative learning

Answer: A

NEW QUESTION # 35
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 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.
D. Use FabricPool to automatically tier all raw data to Azure Blob storage, then mount the blob container to the training VMs.

Answer: C

NEW QUESTION # 36
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. The PVC will remain in a "Pending" state until a PersistentVolume is manually created.
B. Trident will create a 1 Gi volume, as this is the default size for all PVCs.
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.

Answer: C

NEW QUESTION # 37
A distributed training job running on the AIPod fails to start. The MLOps engineer inspects the events for one of the pending training pods and sees the following message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 5m12s default-scheduler 0/4 nodes are available: 4 node(s) had no available volume zone.
The PersistentVolumeClaim (PVC) for this pod specifies a StorageClass that uses the 'ontap-nas' Trident provisioner.
he Trident logs show no errors.
What is the most likely cause of this scheduling failure?

A. The Kubernetes nodes do not have the necessary NFS client utilities installed.
B. The training pod's container image does not exist in the registry.
C. The 'requiredTopology' in the StorageClass is misconfigured, preventing Trident from finding a valid ONTAP backend in the same zone as the available compute nodes.
D. The NetApp ASA storage system is offline.

Answer: C

NEW QUESTION # 38
The firm has acquired a competitor, and the volume of proprietary documents for the "Advisor Assistant" is expected to triple, exceeding the capacity of the current StorageGRID data lake. The architect needs to expand the data lake's capacity non-disruptively.
The current StorageGRID status is:
System_Health: Nominal
Node_Count: 6 (3 Storage Nodes, 3 Admin/Gateway Nodes)
Usable_Capacity: 1.5 PB
Used_Capacity: 1.4 PB (93%)
What is the standard procedure for scaling the capacity of the on-premises NetApp StorageGRID system?

A. Add new storage nodes to the existing StorageGRID system and execute an expansion procedure.
B. Use FabricPool to tier the excess data from StorageGRID to a public cloud provider.
C. Replace the disks in the existing storage nodes with higher-capacity drives.
D. Provision a new, separate StorageGRID system and use BlueXP copy and sync to move the data.

Answer: A

NEW QUESTION # 39
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