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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Topic 1: AI Infrastructure and NetApp Solutions	- Converged workloads 1. AI, HPC, and analytics convergence 2. Shared infrastructure design considerations - AI-ready data infrastructure 1. Data pipeline optimization for AI/ML workloads 2. High-performance storage for AI workloads
Topic 2: AI and Machine Learning Fundamentals	- Algorithm types 1. Reinforcement learning 2. Unsupervised learning 3. Supervised learning - AI, ML, DL concepts 1. Differences between AI, machine learning, and deep learning 2. Training, inference, and prediction workflows
Topic 3: AI Lifecycle and Deployment	- End-to-end AI lifecycle 1. Data ingestion and preparation 2. Model deployment and monitoring 3. Model training and validation - Operational challenges 1. Scalability and performance optimization 2. Data governance and management
Topic 4: Industry Use Cases	- AI applications across industries 1. Healthcare AI 2. Digital twins 3. Autonomous systems and agents

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q82-Q87):

NEW QUESTION # 82
The firm decides to implement a disaster recovery (DR) site for the "Advisor Assistant" application in a secondary data center. The Recovery Point Objective (RPO) is 15 minutes, and the Recovery Time Objective (RTO) is 4 hours. The design must protect both the document data lake and the vector database.
The primary site contains:
- Data Lake: NetApp StorageGRID
- Vector DB: NetApp AFF A-Series
Which combination of technologies and processes provides a complete and robust DR solution?
(Select all that apply.)

A. Rely on tape backups to be shipped to the DR site in the event of a disaster.
B. Use StorageGRID's built-in replication rules to replicate object data from the primary site's grid to a StorageGRID instance at the DR site.
C. Use the NetApp DataOps Toolkit to manually script the failover process.
D. Use NetApp FlexCache at the DR site to cache data from the primary site.
E. Use NetApp SnapMirror to create an asynchronous replication relationship for the AFF A-Series volume containing the vector database, with a schedule of 10 minutes.
F. Use BlueXP disaster recovery to orchestrate and automate the failover and failback workflows for the application and its data dependencies.

Answer: B,E,F

NEW QUESTION # 83
An architect is designing a comprehensive AI platform for a large enterprise. The platform must support the entire data lifecycle, from ingest at the edge to a central data lake, and finally to a high- performance training cluster.
The requirements are:
- Edge Ingest: Data must be collected at remote sites and efficiently replicated to the core.
- Data Lake: A central, petabyte-scale repository for unstructured data, accessible via the S3 protocol.
- Training Cluster: A high-performance compute cluster that requires low-latency, parallel file access to training datasets.
- Data Traceability: All datasets used for training must be immutably versioned.
Which combination of NetApp technologies and protocols should the architect choose to build this solution? (Select all that apply.)

A. Use a NetApp E-Series system with a parallel file system (like BeeGFS) to provide high- performance, parallel file access for the training cluster.
B. Use NetApp ONTAP systems at the edge and NetApp SnapMirror to replicate data to the core data center.
C. Use iSCSI as the primary protocol for the data lake to ensure maximum compatibility.
D. Use NetApp FlexCache to tier cold data from the data lake to the public cloud.
E. Use NetApp Snapshots on the training dataset volumes to create immutable, point-in-time versions for traceability.
F. Use NetApp StorageGRID to create the petabyte-scale, S3-accessible data lake at the core.

Answer: A,B,E,F

NEW QUESTION # 84
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?

A. Implement RDMA over Converged Ethernet (RoCE) and configure GPUDirect Storage.
B. Upgrade the network switches to a model with a larger packet buffer.
C. Isolate the storage traffic on a separate VLAN from the management traffic.
D. Add more 100GbE network ports to the storage controllers.

Answer: A

NEW QUESTION # 85
An internal audit requires the firm to prove the exact state of the financial product documentation that was used by the chatbot to answer a specific query from last Tuesday. The query log shows the request was processed at '2025-07-11T14:30:00Z'. The data pipeline that updates the vector database runs daily at midnight.
Which NetApp technology allows the team to instantly access an immutable, point-in-time version of the vector database volume as it existed on that specific day?

A. NetApp FlexClone
B. NetApp SnapMirror
C. NetApp Snapshots
D. NetApp FabricPool

Answer: C

NEW QUESTION # 86
A research institute is designing an infrastructure to support its entire AI drug discovery pipeline.
The pipeline has two distinct workload requirements:
1. Training: A team of data scientists needs to train several large transformer models simultaneously using a 500 TB dataset of genomic sequences. This process requires maximum data throughput to keep the GPUs saturated.
2. Inference: Once trained, the models are deployed to an internal web portal where researchers submit individual protein sequences for analysis. These queries must return results with the lowest possible latency.
Which infrastructure design best satisfies both requirements? (Choose 2.)

A. Configure QoS minimums on the training volumes to ensure they do not impact inference performance.
B. Use a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.
C. Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.
D. Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.
E. Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.

Answer: C,E

NEW QUESTION # 87
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