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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Overview	15%	- AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - Algorithm Types 1. Supervised learning 2. Unsupervised learning 3. Reinforcement learning - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - AI Deployment Models 1. Edge 2. On-premises 3. Benefits and risks of each model 4. Cloud - Machine Learning Fundamentals 1. Understand the relationship between AI, machine learning, and deep learning 2. Describe machine learning benefits - AI Industry Applications 1. Digital twins 2. Healthcare applications 3. Agents
Topic 2: AI Common Challenges	22%	- Resource Management 1. Sizing storage and compute resources effectively 2. Controlling costs and securing storage - Traceability and Optimization 1. Maximizing performance in demanding AI workloads 2. Ensuring traceability for code, data, and models 3. Optimizing data access and movement
Topic 3: AI Software Architectures	18%	- Scaling and Orchestration 1. Scaling AI workloads with Kubernetes 2. Leveraging BlueXP software tools - Development Tools 1. NetApp DataOps Toolkit 2. Jupyter notebooks vs. pipelines - MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale
Topic 4: AI Lifecycle	27%	- Generative AI Concepts 1. Hallucinations 2. Retrieval Augmented Generation (RAG) 3. Fine-tuning - Predictive AI vs. Generative AI 1. Large Language Models (LLMs) 2. Impact of generative content (text, images, video, decision-making) 3. Distinction between predictive and generative AI - Model Development 1. Model building 2. Inferencing 3. Fine-tuning workflows - Data Preparation 1. NetApp BlueXP Classification 2. XCP and CopySync 3. Data aggregation and cleansing
Topic 5: AI Hardware Architectures	18%	- Networking and Storage 1. Storage architectures for AI 2. Network protocols for AI workloads - NetApp Architectures 1. OVX architectures 2. SuperPOD 3. BasePod - Infrastructure Topologies 1. Data aggregation and compute topologies

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
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 standalone LLM deployed in an air-gapped environment with no access to the document repository.
B. A Retrieval-Augmented Generation (RAG) architecture that retrieves relevant context from a local vector database to enrich prompts sent to the LLM.
C. A predictive AI model trained to forecast market trends.
D. A fine-tuning architecture where a base LLM is continuously retrained on the entire document repository.

Answer: B

NEW QUESTION # 13
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 BlueXP backup and recovery to create backups on StorageGRID, then delete the original volumes from the AFF systems.
B. Train data scientists to use NetApp Snapshots to create point-in-time, read-only versions of their data volumes.
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 FabricPool to automatically tier cold data blocks from the AFF systems to StorageGRID.
F. Implement NetApp FlexClone to create full, writable copies of datasets for each experiment.

Answer: B,E

NEW QUESTION # 14
The architect is designing the complete, automated data pipeline from the on-premises data center to the Azure cloud for this medical imaging project. The design must prioritize security, efficiency, and reproducibility.
Which sequence of actions provides the most robust and automated solution?

A. Use BlueXP classification to scan the on-prem data; use a DataOps Toolkit script to trigger a FlexClone of the source volume for anonymization; use SnapMirror to replicate the anonymized clone to Cloud Volumes ONTAP; then begin training in Azure.
B. Use FabricPool to automatically tier all raw data to Azure Blob storage, then mount the blob container to the training VMs.
C. Use SnapMirror to replicate the raw, sensitive data to Azure first, then run BlueXP classification in the cloud to find PII.
D. Manually copy raw data to Azure, run a cloud-based script to anonymize it, then start training.

Answer: A

NEW QUESTION # 15
What is the primary reason for implementing a hybrid cloud architecture in this medical imaging scenario?

A. To exclusively use cloud-native AI services, eliminating the need for any on-premises hardware.
B. To reduce the overall cost of storage by keeping all data in the public cloud.
C. To simplify network management by using a single, global virtual network spanning the on- premises and cloud environments.
D. To leverage the powerful, elastic GPU compute resources available in the public cloud for model training while keeping sensitive source data on-premises to meet security and HIPAA compliance requirements.

Answer: D

NEW QUESTION # 16
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. Enable Autonomous Ransomware Protection (ARP).
B. Increase the frequency of scheduled Snapshots.
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: A

NEW QUESTION # 17
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