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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

Question 1

The company decides to establish a disaster recovery (DR) site in a secondary data center for the entire Digital Twin platform. The DR plan must protect the HPC data, the AI training data, and the central data lake.
The DR requirements are:
- RPO: 4 hours for all data.
- RTO: 24 hours for the entire platform.
- Process: The failover and failback process should be as automated as possible.
Which combination of technologies provides the most comprehensive DR solution?

A. Deploy a MetroCluster configuration spanning the primary and DR sites for all three storage systems.
B. Use BlueXP backup and recovery to back up all three storage systems (ASA, E-Series, StorageGRID) to a single cloud target.
C. Use NetApp SnapMirror for the ASA, StorageGRID's built-in replication for the data lake, and a host- based tool like 'rsync' for the E-Series/BeeGFS data, all orchestrated via BlueXP disaster recovery.
D. Use NetApp SnapMirror for the ASA, StorageGRID replication for the data lake, and rely on tape backups for the E-Series/BeeGFS data.

Question 2

An organization has a core data center with a large AI training cluster and several remote edge locations for data ingest and local inference. The edge locations frequently need access to the latest models trained in the core data center, but WAN bandwidth is limited and can be unreliable.
Users at the edge are reporting slow model loading times.
An architect reviews the data access logs from an edge site:
Timestamp: 2025-07-11T15:30:00Z
Event: Model_Load_Request
Model_Path: nfs://core-filer.example.com/vol/models/latest_model.pkl
Source_IP: 192.168.100.15 (Edge Server)
Destination_IP: 10.1.1.50 (Core Filer)
Status: SUCCESS
Duration: 3600s (60 minutes)
What is the most likely cause of the slow model loading times at the edge?

A. The core ONTAP filer is using slow, capacity-based disks.
B. The edge server does not have enough RAM to cache the model effectively.
C. The NFS version used between the core and edge is outdated.
D. The model file is being transferred over a slow, high-latency WAN link for every load request.

Question 3

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

Question 4

An architect is designing an AI solution for a European hospital chain to analyze patient diagnostic scans. The project is subject to strict GDPR regulations, which mandate that patient data cannot leave the sovereign territory. The application also requires near-instantaneous results for physicians reviewing the scans in the hospital.
Which deployment model best satisfies these security and performance requirements?

A. An on-premises private cloud for training combined with edge deployments in each hospital for inference.
B. A multi-cloud strategy using different providers for training and inference to avoid vendor lock-in.
C. A hybrid model using a public cloud for training and on-premises for inference.
D. A centralized public cloud deployment in North America for maximum scalability.

Question 5

An online retail company's recommendation engine, which provides real-time product suggestions to users, is experiencing unacceptable latency. The inference application is running on a correctly-sized edge server, but user requests are taking over 500ms to process. An architect reviews the data access pattern and infrastructure diagram.
Application_Location: Edge Server (In-store)
Data_Source_Location: Core Data Center (On-premises ONTAP)
Data_Required_for_Inference: User profile data, product catalog vectors Network_Path: Edge -> WAN -> Core Data Center Observed_Latency: 550ms What is the most likely cause of the high inference latency?

A. The model is too large to fit into the edge server's memory.
B. Every inference request requires a high-latency round trip over the WAN to fetch data from the core data center.
C. The on-premises ONTAP system is not configured for high-throughput.
D. The edge server has insufficient CPU resources to run the model.

Solutions: