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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
A security administrator is reviewing the configuration of a production ONTAP cluster after a compliance audit. The audit requires that all volumes containing financial data must be protected against ransomware. The administrator runs a command to check the status of Autonomous Ransomware Protection (ARP) on a critical volume.
The command and its output are as follows:
cluster-1::> security ransomware anti-ransomware show -vserver svm_finance -volume finance_q1_data Vserver: svm_finance Volume: finance_q1_data Autonomous Ransomware Protection Status: disabled Learning Mode: normal Dry Run Mode: false Action on Detection: notify Based on this output, what is the primary security risk for the 'finance_q1_data' volume?

A. The learning mode is set to "normal," which may not detect zero-day attacks.
B. The volume is not part of any Snapshot schedule.
C. The volume is vulnerable to ransomware because Autonomous Ransomware Protection is not enabled.
D. The action on detection is set only to "notify," which will not block the attack.

Answer: C

NEW QUESTION # 66
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 fine-tuning architecture where a base LLM is continuously retrained on the entire document repository.
B. A predictive AI model trained to forecast market trends.
C. A standalone LLM deployed in an air-gapped environment with no access to the 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

A. Both projects should use NetApp StorageGRID as the primary storage for training to ensure low latency.
B. Project A is a model building task and requires a significantly larger and more powerful compute and storage infrastructure than Project B.
C. Project A requires only a large amount of data, while Project B requires only a large amount of code.
D. Both projects are examples of fine-tuning and have similar infrastructure needs.
E. Project B is a fine-tuning task, which leverages an existing model and requires less data and compute resources than building a model from scratch.

Answer: B,E

NEW QUESTION # 68
The Chief Information Security Officer (CISO) is concerned about the risk of a ransomware attack encrypting the critical vector database hosted on the NetApp AFF A-Series. The CISO wants a solution that can proactively detect and block a live attack in real-time, not just recover from a backup after the fact.
Which NetApp security feature should the architect enable on the vector database volume to meet this requirement?

A. Autonomous Ransomware Protection (ARP)
B. NetApp SnapLock (Compliance Mode)
C. NetApp Volume Encryption (NVE)
D. Multi-Admin Verification (MAV)

Answer: A

NEW QUESTION # 69
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 manual copy process using an S3 client on a bastion host.
C. NetApp BlueXP copy and sync service to create and manage the data synchronization relationship between the S3 source and the NFS/CIFS destination.
D. NetApp SnapMirror to replicate data from the StorageGRID object store to the ASA's file system.
E. A custom Python script using the NetApp DataOps Toolkit to trigger the data movement.

Answer: C,E

NEW QUESTION # 70
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