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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q57-Q62):

NEW QUESTION # 57
A financial services company has deployed a real-time fraud detection model at the edge. The model is designed for low-latency inference. However, monitoring reports indicate that the infrastructure costs are excessively high, and GPU utilization is consistently low. The architect reviews the deployment configuration.
Instance_Type: NVIDIA DGX A100 (8 GPUs)
Storage_Tier: High-Performance All-Flash (NetApp ASA)
Network: 100GbE RoCE
GPU_Utilization_Avg: 5%
Monthly_Cost: $15,000
Workload_Profile: Low-volume, sporadic, real-time predictions
What is the most likely cause of the high costs and low utilization?

A. The compute and storage infrastructure is sized for a large-scale training workload, not a lightweight inference workload.
B. The network latency is too high for an edge deployment.
C. The model was trained using supervised learning, which is inefficient for fraud detection.
D. The storage tier is too slow, causing the GPUs to wait for data.

Answer: A

NEW QUESTION # 58
An architect is designing a fully automated, end-to-end MLOps pipeline on Kubernetes for a computer vision use case. The pipeline must handle everything from data versioning to model deployment.
The required pipeline stages are:
1. Data Versioning: Create a new, immutable version of the master dataset for the pipeline run.
2. Data Preparation: Launch a pod to run a preprocessing script on the versioned data.
3. Model Training: Launch a distributed training job that reads the prepared data from a highperformance volume.
4. Model Deployment: Push the trained model to a production inference service.
Which combination of NetApp and Kubernetes technologies provides the most effective and automated solution for this entire pipeline?

A. Manually create a NetApp Snapshot via System Manager before each pipeline run, and use the NetApp DataOps Toolkit only for the training stage.
B. Use NetApp XCP to copy the data for each stage and configure static PersistentVolumes for each pod.
C. Use the NetApp DataOps Toolkit to create a Snapshot of the source data volume (for versioning), then create a FlexClone PVC from the snapshot for the preparation stage, and finally create a FlexGroup PVC for the training stage.
D. Use a single, large ReadWriteMany PVC for all stages to simplify the pipeline configuration.
E. Use NetApp SnapMirror for data versioning and manually create hostPath volumes for each pipeline stage.

Answer: C

NEW QUESTION # 59
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?

A. The action on detection is set only to "notify," which will not block the attack.
B. The volume is vulnerable to ransomware because Autonomous Ransomware Protection is not enabled.
C. The learning mode is set to "normal," which may not detect zero-day attacks.
D. The volume is not part of any Snapshot schedule.

Answer: B

NEW QUESTION # 60
A company is running its AI training workloads on a NetApp AFF A-Series system. To manage costs, they want to automatically move inactive training datasets and older model checkpoints from the high- performance all-flash tier to a lower-cost object storage tier, such as an on- premises StorageGRID or a public cloud bucket. The process must be transparent to the data scientists and not require changes to their scripts or file paths.
Which two NetApp technologies should be combined to achieve this goal? (Choose 2.)

A. NetApp XCP
B. NetApp FlexCache
C. NetApp SnapMirror
D. NetApp FabricPool
E. NetApp StorageGRID

Answer: D,E

NEW QUESTION # 61
An AI architect is designing a storage solution for a new training cluster. The primary workload consists of training large language models, which involves sequential reads of massive datasets.
The key requirement is to maximize GPU utilization by providing the highest possible data throughput. Cost is a secondary concern to performance.
Which NetApp storage system is the most appropriate choice for this workload?

A. NetApp ASA (All-SAN Array)
B. NetApp C-Series
C. NetApp StorageGRID
D. NetApp E-Series

Answer: A

NEW QUESTION # 62
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