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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Overview	15%	- AI industry use cases and applications - Convergence of AI, high-performance computing, and analytics - AI deployment models: on-premises, cloud, edge - AI, machine learning, and deep learning concepts - Algorithm types: supervised, unsupervised, reinforcement learning
NetApp AI Solutions and Architecture	25%	- NetApp AI-ready infrastructure components - ONTAP integration with AI frameworks - Data management and data pipeline design - Storage architectures for AI workloads - Scalability and performance optimization for AI
AI Lifecycle	27%	- Model training, inference, and optimization - AI lifecycle stages: design, training, deployment, monitoring - Data preparation and management for AI - Predictive vs generative AI - AI governance, ethics, and compliance
Cloud and Hybrid Cloud AI Deployment	18%	- Hybrid and multi-cloud AI architectures - Cloud-native AI solutions and integration - Data mobility and consistency across environments - NetApp cloud data services for AI
Security, Reliability, and Operations	15%	- Data security and access control for AI - High availability and data protection - Cost management and efficiency - Monitoring, logging, and troubleshooting AI environments

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q76-Q81):

NEW QUESTION # 76
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. Use NetApp XCP to periodically move cold data from the AFF systems to StorageGRID.
B. Use BlueXP backup and recovery to create backups on StorageGRID, then delete the original volumes from the AFF systems.
C. Use NetApp SnapMirror to replicate volumes from the AFF systems to StorageGRID for archival.
D. Implement NetApp FlexClone to create full, writable copies of datasets for each experiment.
E. Train data scientists to use NetApp Snapshots to create point-in-time, read-only versions of their data volumes.
F. Implement NetApp FabricPool to automatically tier cold data blocks from the AFF systems to StorageGRID.

Answer: E,F

NEW QUESTION # 77
A data scientist on the team wants to run an experiment on a new vector indexing strategy. To do this, they need a temporary, writable copy of the main vector database. They use the NetApp DataOps Toolkit for Python to automate this process.
The following script is executed:
from netapp_dataops.k8s import clone_pvc
clone_pvc(
source_pvc_name="prod-vector-db-pvc",
new_pvc_name="exp-indexing-clone",
namespace="ds-prod"
)
Assuming the source PVC exists and the user has correct permissions, what is the primary benefit of using this method?

A. It automatically migrates the data from the AFF A-Series to the StorageGRID system for the experiment.
B. It uses NetApp FlexClone technology to create a near-instantaneous, space-efficient writable copy of the volume, accelerating the experimental workflow.
C. It creates a read-only Snapshot, ensuring the integrity of the experimental data.
D. It creates a full physical copy of the data, providing the best performance isolation.

Answer: B

NEW QUESTION # 78
A university is building a shared AI research platform. They have two primary requirements:
1. Performance: A "hot" research area for active model training and development that requires the absolute lowest latency and highest throughput to support multiple, simultaneous GPU- intensive jobs.
The data in this area is around 50 TB.
2. Capacity & Cost: A "cold" data lake to store over 5 PB of raw, unstructured experimental data that is infrequently accessed but must be retained for compliance and future use. This tier must be as costeffective as possible.
Which combination of NetApp hardware and technologies should an architect select to build a complete, optimized, and cost-effective solution? (Select all that apply.)

A. Use NetApp StorageGRID to build the 5 PB cost-effective data lake.
B. Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
C. Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
D. Use a standard 10GbE network for all connectivity to reduce costs.
E. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.
F. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.

Answer: A,B,C,F

NEW QUESTION # 79
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 tiered storage architecture using NetApp FabricPool.
C. GPUDirect Storage support on the storage system.
D. A 10GbE Ethernet network for all data traffic.
E. NetApp StorageGRID as the primary storage for the training datasets.

Answer: A,C

NEW QUESTION # 80
A distributed training job running on the AIPod fails to start. The MLOps engineer inspects the events for one of the pending training pods and sees the following message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 5m12s default-scheduler 0/4 nodes are available: 4 node(s) had no available volume zone.
The PersistentVolumeClaim (PVC) for this pod specifies a StorageClass that uses the 'ontap-nas' Trident provisioner.
he Trident logs show no errors.
What is the most likely cause of this scheduling failure?

A. The Kubernetes nodes do not have the necessary NFS client utilities installed.
B. The 'requiredTopology' in the StorageClass is misconfigured, preventing Trident from finding a valid ONTAP backend in the same zone as the available compute nodes.
C. The training pod's container image does not exist in the registry.
D. The NetApp ASA storage system is offline.

Answer: B

NEW QUESTION # 81
......

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