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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

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

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

3. An architect is designing a scalable, automated MLOps platform using Kubeflow on a Kubernetes cluster. The platform must support the entire AI lifecycle for multiple teams, with different storage requirements at each stage.
The key requirements are:
- Data Ingestion: A pipeline step needs a shared, read-write volume accessible by multiple pods to stage raw data.
- Experimentation: Data scientists need individual, isolated volumes for their Jupyter notebooks.
- Training: Distributed training jobs require a high-performance, parallel-access filesystem for reading training data.
- Automation: All storage must be provisioned automatically via Kubeflow pipeline definitions without manual intervention.
Which combination of technologies and configurations would create the most effective solution?

A) Use the NetApp DataOps Toolkit for Python within the Kubeflow pipeline components to dynamically create and manage Trident PVCs for each stage.
B) Create a single, large NFS volume and mount it to all pods using a static PersistentVolume.
C) Configure multiple Trident backends (e.g., 'ontap-nas' for standard volumes, 'ontap-nas-flexgroup' for parallel access) and corresponding StorageClasses.
D) Rely on hostPath volumes for all storage to ensure the highest performance.
E) Use the NetApp DataOps Toolkit for all storage provisioning, bypassing Trident and Kubernetes PVCs.

4. To comply with the security mandate, the architect must design a process to prevent client PII from the portfolio database from ever being included in the context sent to the LLM. Which two actions are required to build a robust and automated solution for this? (Choose 2.)

A) Implement a data preparation step in the RAG pipeline that programmatically masks or excludes any data fields tagged as PII before the context is constructed.
B) Use BlueXP classification to perform an initial scan of the portfolio database to identify and tag all columns containing PII.
C) Fine-tune the LLM on a sanitized dataset to teach it to ignore PII.
D) Move the entire client portfolio database to an encrypted, air-gapped system.
E) Rely on a disclaimer in the chatbot UI telling advisors not to enter queries related to specific clients.

5. The architect needs to design an efficient data flow to move curated training sets from the central StorageGRID data lake to the high-performance NetApp ASA system used by the AI cluster. The process must be manageable from a single interface and should be automatable. Which two NetApp technologies should be used to implement this data pipeline stage? (Choose 2.)

A) NetApp FabricPool to automatically tier the data from StorageGRID to the ASA.
B) NetApp SnapMirror to replicate data from the StorageGRID object store to the ASA's file system.
C) NetApp BlueXP copy and sync service to create and manage the data synchronization relationship between the S3 source and the NFS/CIFS destination.
D) A custom Python script using the NetApp DataOps Toolkit to trigger the data movement.
E) A manual copy process using an S3 client on a bastion host.

Solutions: