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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Hardware Architectures	18%	- Networking and Storage 1. Network protocols for AI workloads 2. Storage architectures for AI - Infrastructure Topologies 1. Data aggregation and compute topologies - NetApp Architectures 1. SuperPOD 2. BasePod 3. OVX architectures
AI Lifecycle	27%	- Model Development 1. Inferencing 2. Model building 3. Fine-tuning workflows - Generative AI Concepts 1. Hallucinations 2. Fine-tuning 3. Retrieval Augmented Generation (RAG) - Predictive AI vs. Generative AI 1. Impact of generative content (text, images, video, decision-making) 2. Large Language Models (LLMs) 3. Distinction between predictive and generative AI - Data Preparation 1. NetApp BlueXP Classification 2. XCP and CopySync 3. Data aggregation and cleansing
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%	- Machine Learning Fundamentals 1. Describe machine learning benefits 2. Understand the relationship between AI, machine learning, and deep learning - AI Deployment Models 1. Edge 2. Cloud 3. On-premises 4. Benefits and risks of each model - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - Algorithm Types 1. Reinforcement learning 2. Unsupervised learning 3. Supervised learning - AI Industry Applications 1. Healthcare applications 2. Digital twins 3. Agents - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics
AI Common Challenges	22%	- Traceability and Optimization 1. Maximizing performance in demanding AI workloads 2. Optimizing data access and movement 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 (Q45-Q50):

NEW QUESTION # 45
An AI infrastructure team is selecting a storage protocol for a new, high-performance computing (HPC) cluster that will be used for a weather modeling AI application. The application requires extremely high-throughput, parallel access from hundreds of compute nodes to a shared dataset.
The workload characteristics are as follows:
Access_Pattern: Massively parallel reads from many clients.
File_Type: Large, shared data files.
Latency_Sensitivity: High (for metadata operations).
Primary_Requirement: Maximum aggregate throughput.
Which storage protocol is best suited for this workload?

A. A parallel file system like BeeGFS or Lustre.
B. A block-based protocol using iSCSI.
C. A standard file-based system using NFS.
D. An object-based storage system using S3.

Answer: A

NEW QUESTION # 46
Which AI technology is used to generate new, never-before-seen content such as images or text?

A. Predictive AI
B. Reinforcement AI
C. Generative AI
D. Supervised AI

Answer: C

NEW QUESTION # 47
Which storage protocols are commonly used for handling large-scale AI data? (Choose two)

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

Answer: A,C

NEW QUESTION # 48
A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
Which tools are best suited for each of these roles?

A. Both the data scientist and the DevOps engineer should use automated production pipelines.
B. The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.
C. Both the data scientist and the DevOps engineer should use Jupyter Notebooks.
D. The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).

Answer: D

NEW QUESTION # 49
The firm's CFO is concerned about the rising costs of the on-premises AI infrastructure. A storage utilization report shows that of the 200 TB of data on the high-performance AFF A-Series, 150 TB consists of inactive, older versions of product documents that are rarely accessed but must be kept online for regulatory reasons.
The current storage landscape is:
- Performance Tier: NetApp AFF A-Series (200 TB used)
- Capacity Tier: NetApp StorageGRID (1.5 PB used)
What is the most effective and automated solution to reduce the storage cost of the performance tier without impacting data accessibility?

A. Use NetApp SnapMirror to replicate the entire 200 TB volume to the StorageGRID system and then delete the source.
B. Manually identify and delete the 150 TB of inactive data from the AFF A-Series.
C. Purchase an additional, larger AFF A-Series system to gain better storage efficiency through deduplication.
D. Implement NetApp FabricPool to automatically tier the inactive data blocks from the AFF A-Series to the StorageGRID system.

Answer: D

NEW QUESTION # 50
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