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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

1. An AI platform is suffering from poor performance during distributed training jobs. The training data resides on a single, large NFS volume. Monitoring shows that while the overall network throughput to the storage system is high, individual GPU nodes experience significant I/O wait times, and the single ONTAP volume is becoming a performance bottleneck. The goal is to re- architect the storage layout to maximize read parallelism and throughput for the training cluster.
Which two actions should the architect take to address this performance bottleneck? (Choose 2.)

A) Use NetApp FlexCache to create a local cache of the training data on each compute node.
B) Implement a NetApp FlexGroup volume to spread the dataset across multiple constituent volumes and aggregates.
C) Enable QoS maximums on the training volume to limit its IOPS.
D) Replace the NFS protocol with iSCSI for all training data access.
E) Increase the number of network ports connected to the storage controller.

2. An architect is designing the storage and network infrastructure for a new, large-scale AI cluster dedicated to training foundational models. The primary design goal is to achieve the highest possible data throughput and the lowest latency to ensure multi-million dollar GPU resources are never idle. Which two technologies are essential to include in the design to achieve this goal?
(Choose 2.)

A) NetApp StorageGRID as the primary storage for the training datasets.
B) A 10GbE Ethernet network for all data traffic.
C) An InfiniBand or RoCE-capable Ethernet network fabric.
D) GPUDirect Storage support on the storage system.
E) A tiered storage architecture using NetApp FabricPool.

3. A network administrator, attempting to harden the security of the data center, modifies a firewall access control list (ACL). Immediately afterward, the "Advisor Assistant" application pods can no longer mount their required NFS volumes from the AFF A-Series. The MLOps team confirms the pods are stuck in a 'ContainerCreating' state with a 'MountVolume.SetUp failed... connection timed out' error. The administrator provides the new, active firewall rule:
RULE | ACTION | PROTOCOL | SOURCE_IP_RANGE | DEST_IP_RANGE | DEST_PORT --|--|-
|--||--51 | ALLOW | TCP | 10.20.5.0/24 | 10.20.10.0/24 | 2049
What is the most likely reason for the mount failures?

A) The 'SOURCE_IP_RANGE' is incorrect and does not include the Kubernetes pod IP addresses.
B) The firewall rule is blocking the NFS lock manager (NLM) protocol, which is required for file locking.
C) The firewall rule is blocking the Portmapper/RPCbind service (TCP/UDP port 111), which is necessary for the initial NFS mount negotiation.
D) The firewall rule is blocking the NFSv4 protocol, which requires TCP port 2050.

4. 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) Use NetApp FlexCache at the DR site to cache data from the primary site.
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) Rely on tape backups to be shipped to the DR site in the event of a disaster.
D) Use the NetApp DataOps Toolkit to manually script the failover process.
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.

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

Solutions: