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Network Appliance NS0-901 Exam Syllabus Topics:

SectionWeightObjectives
AI Overview15%- Algorithm types: supervised, unsupervised, reinforcement learning
- AI, machine learning, and deep learning concepts
- AI industry use cases and applications
- AI deployment models: on-premises, cloud, edge
- Convergence of AI, high-performance computing, and analytics
Cloud and Hybrid Cloud AI Deployment18%- Hybrid and multi-cloud AI architectures
- Data mobility and consistency across environments
- NetApp cloud data services for AI
- Cloud-native AI solutions and integration
NetApp AI Solutions and Architecture25%- NetApp AI-ready infrastructure components
- Data management and data pipeline design
- Storage architectures for AI workloads
- ONTAP integration with AI frameworks
- Scalability and performance optimization for AI
AI Lifecycle27%- Data preparation and management for AI
- AI lifecycle stages: design, training, deployment, monitoring
- AI governance, ethics, and compliance
- Predictive vs generative AI
- Model training, inference, and optimization
Security, Reliability, and Operations15%- Monitoring, logging, and troubleshooting AI environments
- Data security and access control for AI
- High availability and data protection
- Cost management and efficiency

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q21-Q26):

NEW QUESTION # 21
Which storage protocols are commonly used for handling large-scale AI data? (Choose two)

Answer: C,D


NEW QUESTION # 22
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?

Answer: C


NEW QUESTION # 23
The AI training jobs on the AIPod are performing below expectations. The NVIDIA DGX servers' GPUs show low utilization. A performance analysis reveals that the bottleneck is not the storage system itself, but the network path between the storage and the compute nodes.
The current network configuration is as follows:
Network_Fabric: 100GbE Standard Ethernet
Protocol: NFS over TCP/IP
Data_Path: Storage -> Host CPU -> GPU Memory
Which network architecture enhancement would provide the most significant performance improvement by reducing latency and CPU overhead?

Answer: C


NEW QUESTION # 24
A security administrator is reviewing the configuration of a production ONTAP cluster after a compliance audit. The audit requires that all volumes containing financial data must be protected against ransomware. The administrator runs a command to check the status of Autonomous Ransomware Protection (ARP) on a critical volume.
The command and its output are as follows:
cluster-1::> security ransomware anti-ransomware show -vserver svm_finance -volume finance_q1_data Vserver: svm_finance Volume: finance_q1_data Autonomous Ransomware Protection Status: disabled Learning Mode: normal Dry Run Mode: false Action on Detection: notify Based on this output, what is the primary security risk for the 'finance_q1_data' volume?

Answer: C


NEW QUESTION # 25
What is the primary difference between C-Series and A-Series storage for AI workloads?

Answer: A


NEW QUESTION # 26
......

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