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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Topic 1: AI Lifecycle and Deployment	- Operational challenges 1. Scalability and performance optimization 2. Data governance and management - End-to-end AI lifecycle 1. Model deployment and monitoring 2. Data ingestion and preparation 3. Model training and validation
Topic 2: Industry Use Cases	- AI applications across industries 1. Healthcare AI 2. Digital twins 3. Autonomous systems and agents
Topic 3: AI Infrastructure and NetApp Solutions	- Converged workloads 1. Shared infrastructure design considerations 2. AI, HPC, and analytics convergence - AI-ready data infrastructure 1. Data pipeline optimization for AI/ML workloads 2. High-performance storage for AI workloads
Topic 4: AI and Machine Learning Fundamentals	- AI, ML, DL concepts 1. Differences between AI, machine learning, and deep learning 2. Training, inference, and prediction workflows - Algorithm types 1. Unsupervised learning 2. Reinforcement learning 3. Supervised learning

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q89-Q94):

NEW QUESTION # 89
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 edge server does not have enough RAM to cache the model effectively.
D. The NFS version used between the core and edge is outdated.

Answer: B

NEW QUESTION # 90
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 NetApp DataOps Toolkit is not installed in the container image used for this pipeline step.
B. The Kubernetes cluster is not running NetApp Trident.
C. The Python script is attempting to use the 'clone_pvc' function, but it was not imported from the
'netapp_dataops.k8s' library.
D. The source PVC 'training-data-v2' does not exist.

Answer: C

NEW QUESTION # 91
An AI infrastructure architect is tasked with designing a solution to address two critical challenges in a large, multi-petabyte AI environment:
1. Cost: A significant portion of the data on the high-performance all-flash storage is inactive but must remain online. The cost of storing this cold data on the performance tier is prohibitive.
2. Traceability: Data scientists need a simple, space-efficient way to version their datasets at key points in their workflow to ensure reproducibility.
The environment consists of NetApp AFF A-Series and NetApp StorageGRID systems.
Which combination of NetApp technologies should the architect implement to solve both challenges simultaneously? (Select all that apply.)

A. Train data scientists to use NetApp Snapshots to create point-in-time, read-only versions of their data volumes.
B. Implement NetApp FabricPool to automatically tier cold data blocks from the AFF systems to StorageGRID.
C. Use NetApp SnapMirror to replicate volumes from the AFF systems to StorageGRID for archival.
D. Use NetApp XCP to periodically move cold data from the AFF systems to StorageGRID.
E. Implement NetApp FlexClone to create full, writable copies of datasets for each experiment.
F. Use BlueXP backup and recovery to create backups on StorageGRID, then delete the original volumes from the AFF systems.

Answer: A,B

NEW QUESTION # 92
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 firewall rule is blocking the Portmapper/RPCbind service (TCP/UDP port 111), which is necessary for the initial NFS mount negotiation.
B. The 'SOURCE_IP_RANGE' is incorrect and does not include the Kubernetes pod IP addresses.
C. The firewall rule is blocking the NFS lock manager (NLM) protocol, which is required for file locking.
D. The firewall rule is blocking the NFSv4 protocol, which requires TCP port 2050.

Answer: A

NEW QUESTION # 93
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. An InfiniBand or RoCE-capable Ethernet network fabric.
B. A 10GbE Ethernet network for all data traffic.
C. GPUDirect Storage support on the storage system.
D. A tiered storage architecture using NetApp FabricPool.
E. NetApp StorageGRID as the primary storage for the training datasets.

Answer: A,C

NEW QUESTION # 94
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