Network Appliance NetApp Certified AI Expert NS0-901

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Network Appliance NetApp Certified AI Expert Sample Questions:

1. 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 StorageGRID's built-in replication rules to replicate object data from the primary site's grid to a StorageGRID instance at the DR site.
B) Use the NetApp DataOps Toolkit to manually script the failover process.
C) Use NetApp FlexCache at the DR site to cache data from the primary site.
D) Use BlueXP disaster recovery to orchestrate and automate the failover and failback workflows for the application and its data dependencies.
E) Rely on tape backups to be shipped to the DR site in the event of a disaster.
F) 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.

2. A development team is building a generative AI application that must answer questions based on a constantly changing internal knowledge base of company documents. They want to provide the model with up-to-date information without altering its core weights and capabilities.
Which approach is most suitable for this requirement?

A) Fine-tuning, which retrains the model's last few layers on the new documents.
B) Unsupervised learning, which clusters the documents into topics for the model to browse.
C) Predictive AI, which analyzes the documents to forecast future questions.
D) Retrieval-Augmented Generation (RAG), which fetches relevant information from the documents at query time to inform the model's response.

3. A national research laboratory is investing in a turnkey AI infrastructure solution. Their primary goal is to eliminate the complexity and risk of designing and integrating the compute, network, and storage components themselves. The solution must be pre-validated by the vendors to deliver predictable, linear performance as they scale from one to multiple compute nodes. Which two options represent this type of pre-validated, converged infrastructure solution for AI? (Choose two)

A) A single NetApp FAS system connected directly to a single server.
B) A public cloud provider's general-purpose virtual machine instances.
C) A custom-built server rack with components sourced from various different vendors.
D) NetApp AIPod, which integrates NVIDIA DGX servers with NetApp storage and networking.
E) A Cisco and NetApp FlexPod for AI solution.

4. The firm has acquired a competitor, and the volume of proprietary documents for the "Advisor Assistant" is expected to triple, exceeding the capacity of the current StorageGRID data lake. The architect needs to expand the data lake's capacity non-disruptively.
The current StorageGRID status is:
System_Health: Nominal
Node_Count: 6 (3 Storage Nodes, 3 Admin/Gateway Nodes)
Usable_Capacity: 1.5 PB
Used_Capacity: 1.4 PB (93%)
What is the standard procedure for scaling the capacity of the on-premises NetApp StorageGRID system?

A) Add new storage nodes to the existing StorageGRID system and execute an expansion procedure.
B) Replace the disks in the existing storage nodes with higher-capacity drives.
C) Provision a new, separate StorageGRID system and use BlueXP copy and sync to move the data.
D) Use FabricPool to tier the excess data from StorageGRID to a public cloud provider.

5. A research lab uses a fleet of autonomous drones to collect high-resolution aerial imagery for agricultural analysis. The drones land at a remote edge location and offload their data. The AI models for image analysis are trained at a central data center. The team is using NetApp SnapMirror to replicate the data from the edge to the core. However, the data scientists are complaining that the datasets arriving at the data center are often incomplete or corrupted.
An administrator reviews the SnapMirror configuration and status via the BlueXP API:
{
"source": { "workingEnvironmentId": "OnPrem-Edge-Filer-1", "volumeName": "drone_data_raw" },
"destination": { "workingEnvironmentId": "Core-Datacenter-A800", "volumeName":
"drone_data_replicated" },
"mirrorState": "broken-off",
"relationshipStatus": "idle",
"unhealthyReason": "Transfer failed. Destination volume is out of space.", "lastTransferInfo": {
"transferError": "No space left on device"
}
}
What is the direct cause of the incomplete datasets at the data center?

A) The SnapMirror relationship is broken because the destination volume at the core data center has run out of capacity.
B) The network connection between the edge and the core is unreliable.
C) The BlueXP Connector does not have the correct permissions to manage the SnapMirror relationship.
D) The source volume at the edge location has become corrupted.

Solutions: