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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q84-Q89):

NEW QUESTION # 84
An architect is designing an AI solution for a European hospital chain to analyze patient diagnostic scans. The project is subject to strict GDPR regulations, which mandate that patient data cannot leave the sovereign territory. The application also requires near-instantaneous results for physicians reviewing the scans in the hospital.
Which deployment model best satisfies these security and performance requirements?

A. A centralized public cloud deployment in North America for maximum scalability.
B. An on-premises private cloud for training combined with edge deployments in each hospital for inference.
C. A multi-cloud strategy using different providers for training and inference to avoid vendor lock-in.
D. A hybrid model using a public cloud for training and on-premises for inference.

Answer: B

NEW QUESTION # 85
Which storage protocols are commonly used for handling large-scale AI data? (Choose two)

A. POSIX-based file systems
B. Parallel file systems
C. File-based systems
D. Object-based storage

Answer: B,D

NEW QUESTION # 86
To meet HIPAA compliance, the first step in the data pipeline is to identify all medical scans that contain embedded PII. The solution must be automated and capable of scanning data in-place on the on-premises ASA system.
Which two technologies should be used to accomplish this identification and tagging task?
(Choose 2.)

A. A manual review process where technicians visually inspect each scan for PII.
B. NetApp BlueXP classification, configured to scan the on-premises ASA working environment.
C. A custom "PII" category within BlueXP classification to identify specific medical record number formats.
D. A custom Python script that uses regular expressions to search file contents.
E. NetApp SnapLock to make the source data immutable before scanning.

Answer: B,C

NEW QUESTION # 87
An MLOps team uses a variety of platforms to manage their AI workloads. They need to understand the primary function of each tool within their ecosystem. Which statement best describes the role of an MLOps/LLMOps platform like Kubeflow or Run:AI?

A. They are orchestration and management platforms that automate and streamline the entire AI/ML lifecycle, from data preparation and model training to deployment and monitoring.
B. They are integrated development environments (IDEs) used exclusively for writing Python code.
C. They are specialized storage systems designed to hold large datasets for training.
D. They are networking protocols designed to accelerate data transfer between GPUs.

Answer: A

NEW QUESTION # 88
An MLOps team is using the BlueXP Workload Factory to automate the deployment of a Jupyter notebook environment for a new data scientist. The deployment job fails. The team reviews the job details in the BlueXP UI.
Job ID: 78a1c-2b3d-4e5f
Service: BlueXP Workload Factory
Action: Deploy JupyterHub on Kubernetes
Status: FAILED
Error Message: "Failed to create PersistentVolumeClaim 'jupyter-pvc-user01'.
User 'service-account- bluexp' does not have permission to 'create' resource
'persistentvolumeclaims' in cluster 'k8s-prod- cluster-01'."
What is the most direct way to resolve this issue?

A. Restart the BlueXP Connector to refresh its permissions.
B. Increase the storage quota for the 'jupyter-pvc-user01' PVC.
C. Modify the Kubernetes RBAC (Role-Based Access Control) configuration to grant the 'service- account-bluexp' the necessary permissions to create PVCs in the target cluster.
D. Manually create the PersistentVolumeClaim using 'kubectl'.

Answer: C

NEW QUESTION # 89
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