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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q80-Q85):

NEW QUESTION # 80
An AI infrastructure engineer is troubleshooting a poorly performing distributed training job. The job is running across multiple nodes, each equipped with powerful GPUs. The engineer observes that overall GPU utilization is unexpectedly low. System-level monitoring on the compute nodes provides the following metrics during a training run.
avg_gpu_utilization: 25%
avg_cpu_iowait_percent: 65%
avg_network_bandwidth_util: 95% (on a 10GbE network)
storage_array_latency: <1ms
Given these metrics, what is the most likely bottleneck causing the low GPU utilization?

A. The network connecting the compute nodes and storage is saturated and has become the primary bottleneck.
B. The CPU is underpowered and cannot preprocess the data fast enough for the GPUs.
C. The GPUs are faulty and cannot process data at their rated speed.
D. The storage array is too slow and cannot serve data quickly enough.

Answer: A

NEW QUESTION # 81
A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
Which tools are best suited for each of these roles?

A. Both the data scientist and the DevOps engineer should use Jupyter Notebooks.
B. The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.
C. Both the data scientist and the DevOps engineer should use automated production pipelines.
D. The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).

Answer: D

NEW QUESTION # 82
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. Use BlueXP backup and recovery to back up the volumes to the cloud more frequently.
B. Increase the frequency of scheduled Snapshots.
C. Enable Autonomous Ransomware Protection (ARP).
D. Enable Multi-Admin Verification (MAV) on all volumes.

Answer: C

NEW QUESTION # 83
An AI platform administrator is trying to deploy a new GenAI toolkit using the BlueXP Workload Factory. The deployment fails, and the administrator examines the API response from BlueXP.
{
"jobId": "we-deploy-genai-987zy",
"status": "FAILED",
"statusCode": 403,
"message":
"Forbidden: The service principal or user account used by the Connector does not have the required permissions on the target subscription to create a new resource group.
Required permission: 'Microsoft.Resources/subscriptions/resourcegroups/write'." } Based on this API response, what is the root cause of the deployment failure?

A. The selected ONTAP version does not support the GenAI toolkit.
B. The BlueXP Connector is offline and cannot communicate with the cloud provider.
C. The GenAI toolkit requires a specific license that has not been added to the BlueXP digital wallet.
D. The Azure service principal associated with the BlueXP Connector lacks the necessary IAM role to create resource groups.

Answer: D

NEW QUESTION # 84
An AI infrastructure architect is tasked with designing a solution to address two critical challenges in a large, multi-petabyte AI environment:
1. Cost: A significant portion of the data on the high-performance all-flash storage is inactive but must remain online. The cost of storing this cold data on the performance tier is prohibitive.
2. Traceability: Data scientists need a simple, space-efficient way to version their datasets at key points in their workflow to ensure reproducibility.
The environment consists of NetApp AFF A-Series and NetApp StorageGRID systems.
Which combination of NetApp technologies should the architect implement to solve both challenges simultaneously? (Select all that apply.)

A. Use NetApp SnapMirror to replicate volumes from the AFF systems to StorageGRID for archival.
B. Use NetApp XCP to periodically move cold data from the AFF systems to StorageGRID.
C. Train data scientists to use NetApp Snapshots to create point-in-time, read-only versions of their data volumes.
D. Implement NetApp FabricPool to automatically tier cold data blocks from the AFF systems to StorageGRID.
E. Implement NetApp FlexClone to create full, writable copies of datasets for each experiment.
F. Use BlueXP backup and recovery to create backups on StorageGRID, then delete the original volumes from the AFF systems.

Answer: C,D

NEW QUESTION # 85
......

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