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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Lifecycle	27%	- Data preparation and management for AI - Model training, inference, and optimization - AI governance, ethics, and compliance - Predictive vs generative AI - AI lifecycle stages: design, training, deployment, monitoring
Topic 2: Cloud and Hybrid Cloud AI Deployment	18%	- Data mobility and consistency across environments - Hybrid and multi-cloud AI architectures - Cloud-native AI solutions and integration - NetApp cloud data services for AI
Topic 3: NetApp AI Solutions and Architecture	25%	- Storage architectures for AI workloads - Scalability and performance optimization for AI - NetApp AI-ready infrastructure components - ONTAP integration with AI frameworks - Data management and data pipeline design
Topic 4: Security, Reliability, and Operations	15%	- High availability and data protection - Cost management and efficiency - Monitoring, logging, and troubleshooting AI environments - Data security and access control for AI
Topic 5: AI Overview	15%	- Convergence of AI, high-performance computing, and analytics - AI, machine learning, and deep learning concepts - AI deployment models: on-premises, cloud, edge - AI industry use cases and applications - Algorithm types: supervised, unsupervised, reinforcement learning

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q27-Q32):

NEW QUESTION # 27
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 FlexCache
C. NetApp XCP
D. NetApp FabricPool
E. NetApp StorageGRID

Answer: D,E

NEW QUESTION # 28
Given the firm's requirements for using a private, constantly updated knowledge base and the strict mandate for data traceability, which AI architecture is the most appropriate foundation for the "Advisor Assistant" chatbot?

A. A predictive AI model trained to forecast market trends.
B. A standalone LLM deployed in an air-gapped environment with no access to the document repository.
C. A fine-tuning architecture where a base LLM is continuously retrained on the entire document repository.
D. A Retrieval-Augmented Generation (RAG) architecture that retrieves relevant context from a local vector database to enrich prompts sent to the LLM.

Answer: D

NEW QUESTION # 29
An organization recently suffered a ransomware attack that encrypted several volumes on their primary ONTAP storage system, including a critical volume containing curated training data. The security team needs to implement a solution that can proactively detect and block ransomware- like file I/O patterns and automatically create a secure Snapshot copy before any damage is done.
The current ONTAP configuration is as follows:
ONTAP_Version: 9.12.1
Security_Features: SnapLock (Compliance Mode) on archive volumes
Anti-Virus_Scan: Enabled (Vscan)
Ransomware_Detection: Not configured
Which ONTAP feature should be enabled to provide this proactive, automated protection?

A. Increase the frequency of scheduled Snapshots.
B. Enable Autonomous Ransomware Protection (ARP).
C. Use BlueXP backup and recovery to back up the volumes to the cloud more frequently.
D. Enable Multi-Admin Verification (MAV) on all volumes.

Answer: B

NEW QUESTION # 30
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. GPUDirect Storage support on the storage system.
B. An InfiniBand or RoCE-capable Ethernet network fabric.
C. A 10GbE Ethernet network for all data traffic.
D. A tiered storage architecture using NetApp FabricPool.
E. NetApp StorageGRID as the primary storage for the training datasets.

Answer: A,B

NEW QUESTION # 31
What is the primary role of NetApp Trident in a Kubernetes environment designed for AI workloads?

A. To act as a dynamic storage orchestrator, provisioning persistent storage from NetApp backends on- demand for containerized applications.
B. To function as a container runtime interface for executing AI models.
C. To directly accelerate GPU computations using specialized drivers.
D. To provide a web-based IDE, like Jupyter, for data scientists to develop models.

Answer: A

NEW QUESTION # 32
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