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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: NetApp AI Solutions and Architecture	25%	- Storage architectures for AI workloads - ONTAP integration with AI frameworks - NetApp AI-ready infrastructure components - Data management and data pipeline design - Scalability and performance optimization for AI
Topic 2: Cloud and Hybrid Cloud AI Deployment	18%	- Cloud-native AI solutions and integration - Hybrid and multi-cloud AI architectures - NetApp cloud data services for AI - Data mobility and consistency across environments
Topic 3: AI Lifecycle	27%	- Predictive vs generative AI - AI governance, ethics, and compliance - Model training, inference, and optimization - AI lifecycle stages: design, training, deployment, monitoring - Data preparation and management for AI
Topic 4: AI Overview	15%	- AI deployment models: on-premises, cloud, edge - AI, machine learning, and deep learning concepts - AI industry use cases and applications - Algorithm types: supervised, unsupervised, reinforcement learning - Convergence of AI, high-performance computing, and analytics
Topic 5: Security, Reliability, and Operations	15%	- Data security and access control for AI - Monitoring, logging, and troubleshooting AI environments - Cost management and efficiency - High availability and data protection

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q42-Q47):

NEW QUESTION # 42
An organization recently suffered a ransomware attack that encrypted several volumes on their primary ONTAP storage system, including a critical volume containing curated training data. The security team needs to implement a solution that can proactively detect and block ransomware- like file I/O patterns and automatically create a secure Snapshot copy before any damage is done.
The current ONTAP configuration is as follows:
ONTAP_Version: 9.12.1
Security_Features: SnapLock (Compliance Mode) on archive volumes
Anti-Virus_Scan: Enabled (Vscan)
Ransomware_Detection: Not configured
Which ONTAP feature should be enabled to provide this proactive, automated protection?

A. Enable Multi-Admin Verification (MAV) on all volumes.
B. Increase the frequency of scheduled Snapshots.
C. Use BlueXP backup and recovery to back up the volumes to the cloud more frequently.
D. Enable Autonomous Ransomware Protection (ARP).

Answer: D

NEW QUESTION # 43
Which of the following are typical requirements for inferencing in AI models? (Choose two)

A. Pre-trained models
B. Cloud-based inference only
C. Extensive training data
D. Loading the model into memory

Answer: A,D

NEW QUESTION # 44
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. An InfiniBand or RoCE-capable Ethernet network fabric.
B. GPUDirect Storage support on the storage system.
C. NetApp StorageGRID as the primary storage for the training datasets.
D. A 10GbE Ethernet network for all data traffic.
E. A tiered storage architecture using NetApp FabricPool.

Answer: A,B

NEW QUESTION # 45
What is the primary reason for implementing a hybrid cloud architecture in this medical imaging scenario?

A. To reduce the overall cost of storage by keeping all data in the public cloud.
B. To leverage the powerful, elastic GPU compute resources available in the public cloud for model training while keeping sensitive source data on-premises to meet security and HIPAA compliance requirements.
C. To exclusively use cloud-native AI services, eliminating the need for any on-premises hardware.
D. To simplify network management by using a single, global virtual network spanning the on- premises and cloud environments.

Answer: B

NEW QUESTION # 46
An MLOps team is using the BlueXP Workload Factory to automate the deployment of a Jupyter notebook environment for a new data scientist. The deployment job fails. The team reviews the job details in the BlueXP UI.
Job ID: 78a1c-2b3d-4e5f
Service: BlueXP Workload Factory
Action: Deploy JupyterHub on Kubernetes
Status: FAILED
Error Message: "Failed to create PersistentVolumeClaim 'jupyter-pvc-user01'.
User 'service-account- bluexp' does not have permission to 'create' resource
'persistentvolumeclaims' in cluster 'k8s-prod- cluster-01'."
What is the most direct way to resolve this issue?

A. Restart the BlueXP Connector to refresh its permissions.
B. Manually create the PersistentVolumeClaim using 'kubectl'.
C. Modify the Kubernetes RBAC (Role-Based Access Control) configuration to grant the 'service- account-bluexp' the necessary permissions to create PVCs in the target cluster.
D. Increase the storage quota for the 'jupyter-pvc-user01' PVC.

Answer: C

NEW QUESTION # 47
......

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