Network Appliance NS0-901 : NetApp Certified AI Expert Exam

The firm decides to expand the "Advisor Assistant" project to a new team in a different department. This team needs its own isolated environment. An MLOps engineer attempts to submit a new GPU- intensive job for the new team, but it remains pending. The engineer checks the Run:AI scheduler logs and finds the following entry:
time="2025-07-11T16:45:00Z" level=info msg="Job ds-new-team-job1 cannot be scheduled.
Project 'new-team-project' has exceeded its GPU quota. Quota: 0, Requested: 1, Used: 0." What is the root cause of the scheduling failure?

• A. The Kubernetes cluster has no available GPUs.
• B. The Run:AI scheduler is offline and cannot process new jobs.
• C. The job is requesting a specific type of GPU that is not available in the cluster.
• D. A Run:AI project quota has been configured for the new team, but it has been set to zero, effectively blocking them from using any GPU resources.

Which of the following techniques are used to maximize GPU utilization in AI workloads? (Choose two)

• A. Utilizing CPU-based workloads for all tasks
• B. Balancing compute and storage workloads efficiently
• C. Scheduling GPU-intensive tasks during off-peak hours
• D. Using larger batch sizes

Which of the following best describes the difference between data lakes, data warehouses, and lakehouses?

• A. Data lakes store data in cloud storage, data warehouses store it in traditional databases, and lakehouses store it in external drives.
• B. Data lakes store structured data, data warehouses store unstructured data, and lakehouses store only real-time data.
• C. Data lakes store metadata, data warehouses store transaction data, and lakehouses store archival data.
• D. Data lakes store raw, unstructured data, data warehouses store structured data, and lakehouses combine the features of both.

The firm's CFO is concerned about the rising costs of the on-premises AI infrastructure. A storage utilization report shows that of the 200 TB of data on the high-performance AFF A-Series, 150 TB consists of inactive, older versions of product documents that are rarely accessed but must be kept online for regulatory reasons.
The current storage landscape is:
- Performance Tier: NetApp AFF A-Series (200 TB used)
- Capacity Tier: NetApp StorageGRID (1.5 PB used)
What is the most effective and automated solution to reduce the storage cost of the performance tier without impacting data accessibility?

• A. Manually identify and delete the 150 TB of inactive data from the AFF A-Series.
• B. Implement NetApp FabricPool to automatically tier the inactive data blocks from the AFF A-Series to the StorageGRID system.
• C. Purchase an additional, larger AFF A-Series system to gain better storage efficiency through deduplication.
• D. Use NetApp SnapMirror to replicate the entire 200 TB volume to the StorageGRID system and then delete the source.

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