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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
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
AI Lifecycle and Deployment	- End-to-end AI lifecycle 1. Model deployment and monitoring 2. Data ingestion and preparation 3. Model training and validation - Operational challenges 1. Scalability and performance optimization 2. Data governance and management
Industry Use Cases	- AI applications across industries 1. Healthcare AI 2. Digital twins 3. Autonomous systems and agents
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 (Q26-Q31):

NEW QUESTION # 26
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. NetApp StorageGRID as the primary storage for the training datasets.
C. A 10GbE Ethernet network for all data traffic.
D. A tiered storage architecture using NetApp FabricPool.
E. GPUDirect Storage support on the storage system.

Answer: A,E

NEW QUESTION # 27
What is the primary architectural advantage of using a NetApp AIPod with NVIDIA DGX servers for the AI training cluster, as described in the scenario?

A. It exclusively uses object storage, which simplifies access for data scientists using S3-native tools.
B. It prioritizes CPU performance over GPU performance for traditional machine learning algorithms.
C. It is a reference architecture that is pre-validated by NetApp and NVIDIA to eliminate design complexity and ensure predictable performance for AI workloads.
D. It is designed for small-scale, departmental AI projects and cannot be scaled.

Answer: C

NEW QUESTION # 28
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 SnapMirror
B. NetApp StorageGRID
C. NetApp XCP
D. NetApp FabricPool
E. NetApp FlexCache

Answer: B,D

NEW QUESTION # 29
A team has deployed a Retrieval-Augmented Generation (RAG) system to answer customer queries. Recently, users have complained that the answers provided by the chatbot are outdated and do not reflect the latest product updates. An architect investigates and finds the following status log from the RAG pipeline's data ingestion monitor.
Timestamp: 2025-07-11T14:00:00Z
System: RAG Pipeline Monitor
Status: WARNING
Message: Vector DB freshness check failed.
Source data appears stale.
Vector_DB_Last_Update: 2025-06-10T08:00:00Z
Knowledge_Base_Last_Modified: 2025-07-11T13:15:00Z
Data_Sync_Service: BlueXP copy and sync
Sync_Job_Status: Succeeded
Based on the log, what is the most likely cause of the outdated answers?

A. The knowledge base itself has not been updated with the latest product information.
B. The LLM needs to be fine-tuned with the new product information.
C. The BlueXP copy and sync service is failing to copy data to the staging area.
D. The process that converts staged documents into vectors and updates the vector database is not running.

Answer: D

NEW QUESTION # 30
The architect needs to design an efficient data flow to move curated training sets from the central StorageGRID data lake to the high-performance NetApp ASA system used by the AI cluster. The process must be manageable from a single interface and should be automatable. Which two NetApp technologies should be used to implement this data pipeline stage? (Choose 2.)

A. NetApp FabricPool to automatically tier the data from StorageGRID to the ASA.
B. A custom Python script using the NetApp DataOps Toolkit to trigger the data movement.
C. A manual copy process using an S3 client on a bastion host.
D. NetApp BlueXP copy and sync service to create and manage the data synchronization relationship between the S3 source and the NFS/CIFS destination.
E. NetApp SnapMirror to replicate data from the StorageGRID object store to the ASA's file system.

Answer: B,D

NEW QUESTION # 31
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