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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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
Topic 2: 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 Deployment Models 1. Edge 2. Benefits and risks of each model 3. Cloud 4. On-premises - AI Industry Applications 1. Digital twins 2. Healthcare applications 3. Agents - Algorithm Types 1. Reinforcement learning 2. Supervised learning 3. Unsupervised learning - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads
Topic 3: AI Lifecycle	27%	- Model Development 1. Inferencing 2. Model building 3. Fine-tuning workflows - 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 - Data Preparation 1. XCP and CopySync 2. NetApp BlueXP Classification 3. Data aggregation and cleansing
Topic 4: AI Hardware Architectures	18%	- Infrastructure Topologies 1. Data aggregation and compute topologies - NetApp Architectures 1. BasePod 2. SuperPOD 3. OVX architectures - Networking and Storage 1. Network protocols for AI workloads 2. Storage architectures for AI
Topic 5: AI Software Architectures	18%	- 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 - Scaling and Orchestration 1. Scaling AI workloads with Kubernetes 2. Leveraging BlueXP software tools

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최신 NetApp Certified AI Expert NS0-901 무료샘플문제 (Q95-Q100):

질문 # 95
An AI architect is planning the resource allocation for a new project. The primary task is to process millions of unlabeled customer reviews to identify naturally occurring groups or themes without any prior guidance.
The project requirements are summarized below:
Task: Discover hidden patterns in text data
Input_Data: 10 million unlabeled text reviews
Output: Clustered groups of related reviews
Supervision: None
Which type of machine learning algorithm is required for this task?

A. Unsupervised learning
B. Supervised learning
C. Reinforcement learning
D. Predictive learning

정답：A

A. Recommend training a new LLM from scratch to ensure both brand voice and factual accuracy are built-in.
B. Prioritize fine-tuning to embed the company's brand voice and persona into the model.
C. Prioritize a RAG architecture to meet the critical requirements for factual accuracy and data traceability.
D. Propose a hybrid approach where a base model is first lightly fine-tuned for persona, then used within a RAG system for factual grounding.
E. Use RAG exclusively, as prompt engineering alone can fully replicate a specific brand voice and persona.

정답：C,D

질문 # 97
The company decides to establish a disaster recovery (DR) site in a secondary data center for the entire Digital Twin platform. The DR plan must protect the HPC data, the AI training data, and the central data lake.
The DR requirements are:
- RPO: 4 hours for all data.
- RTO: 24 hours for the entire platform.
- Process: The failover and failback process should be as automated as possible.
Which combination of technologies provides the most comprehensive DR solution?

A. Deploy a MetroCluster configuration spanning the primary and DR sites for all three storage systems.
B. Use NetApp SnapMirror for the ASA, StorageGRID's built-in replication for the data lake, and a host- based tool like 'rsync' for the E-Series/BeeGFS data, all orchestrated via BlueXP disaster recovery.
C. Use NetApp SnapMirror for the ASA, StorageGRID replication for the data lake, and rely on tape backups for the E-Series/BeeGFS data.
D. Use BlueXP backup and recovery to back up all three storage systems (ASA, E-Series, StorageGRID) to a single cloud target.

정답：B

질문 # 98
An AI architect is designing a storage solution for a new training cluster. The primary workload consists of training large language models, which involves sequential reads of massive datasets.
The key requirement is to maximize GPU utilization by providing the highest possible data throughput. Cost is a secondary concern to performance.
Which NetApp storage system is the most appropriate choice for this workload?

A. NetApp C-Series
B. NetApp ASA (All-SAN Array)
C. NetApp StorageGRID
D. NetApp E-Series

정답：B

질문 # 99
A university is building a shared AI research platform. They have two primary requirements:
1. Performance: A "hot" research area for active model training and development that requires the absolute lowest latency and highest throughput to support multiple, simultaneous GPU- intensive jobs.
The data in this area is around 50 TB.
2. Capacity & Cost: A "cold" data lake to store over 5 PB of raw, unstructured experimental data that is infrequently accessed but must be retained for compliance and future use. This tier must be as costeffective as possible.
Which combination of NetApp hardware and technologies should an architect select to build a complete, optimized, and cost-effective solution? (Select all that apply.)

A. Use a standard 10GbE network for all connectivity to reduce costs.
B. Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
C. Use NetApp StorageGRID to build the 5 PB cost-effective data lake.
D. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.
E. Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
F. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.

정답：B,C,D,E

질문 # 100
......

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