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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

1. An architect is designing an AI solution for a European hospital chain to analyze patient diagnostic scans. The project is subject to strict GDPR regulations, which mandate that patient data cannot leave the sovereign territory. The application also requires near-instantaneous results for physicians reviewing the scans in the hospital.
Which deployment model best satisfies these security and performance requirements?

A) A centralized public cloud deployment in North America for maximum scalability.
B) An on-premises private cloud for training combined with edge deployments in each hospital for inference.
C) A multi-cloud strategy using different providers for training and inference to avoid vendor lock-in.
D) A hybrid model using a public cloud for training and on-premises for inference.

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

3. A financial services company has deployed a real-time fraud detection model at the edge. The model is designed for low-latency inference. However, monitoring reports indicate that the infrastructure costs are excessively high, and GPU utilization is consistently low. The architect reviews the deployment configuration.
Instance_Type: NVIDIA DGX A100 (8 GPUs)
Storage_Tier: High-Performance All-Flash (NetApp ASA)
Network: 100GbE RoCE
GPU_Utilization_Avg: 5%
Monthly_Cost: $15,000
Workload_Profile: Low-volume, sporadic, real-time predictions
What is the most likely cause of the high costs and low utilization?

A) The network latency is too high for an edge deployment.
B) The compute and storage infrastructure is sized for a large-scale training workload, not a lightweight inference workload.
C) The storage tier is too slow, causing the GPUs to wait for data.
D) The model was trained using supervised learning, which is inefficient for fraud detection.

4. A company is running its AI training workloads on a NetApp AFF A-Series system. To manage costs, they want to automatically move inactive training datasets and older model checkpoints from the high- performance all-flash tier to a lower-cost object storage tier, such as an on- premises StorageGRID or a public cloud bucket. The process must be transparent to the data scientists and not require changes to their scripts or file paths.
Which two NetApp technologies should be combined to achieve this goal? (Choose 2.)

A) NetApp FabricPool
B) NetApp FlexCache
C) NetApp StorageGRID
D) NetApp SnapMirror
E) NetApp XCP

5. An MLOps engineer is troubleshooting a failed Kubeflow pipeline step. The step was designed to create a clone of a dataset for a training job using the NetApp DataOps Toolkit. The pod logs for the failed pipeline step show the following:
Traceback (most recent call last):
File "create_clone.py", line 15, in <module>
clone_pvc(source_pvc_name="training-data-v2", new_pvc_name="train-job-34a-data") NameError: name 'clone_pvc' is not defined The engineer reviews the Python script for the pipeline step:
# create_clone.py
import os
from netapp_dataops.k8s import create_pvc
# Other code
print("Cloning source dataset for training run...")
clone_pvc(
source_pvc_name="training-data-v2",
new_pvc_name="train-job-34a-data"
)
print("Clone created successfully.")
What is the cause of the error?

A) The Kubernetes cluster is not running NetApp Trident.
B) The source PVC 'training-data-v2' does not exist.
C) The NetApp DataOps Toolkit is not installed in the container image used for this pipeline step.
D) The Python script is attempting to use the 'clone_pvc' function, but it was not imported from the
'netapp_dataops.k8s' library.

Solutions: