NS0-901 New Dumps Questions, Practice NS0-901 Test Online

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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Lifecycle	27%	- Model Development 1. Fine-tuning workflows 2. Inferencing 3. Model building - Data Preparation 1. Data aggregation and cleansing 2. NetApp BlueXP Classification 3. XCP and CopySync - 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 - Generative AI Concepts 1. Fine-tuning 2. Hallucinations 3. Retrieval Augmented Generation (RAG)
AI Common Challenges	22%	- Resource Management 1. Sizing storage and compute resources effectively 2. Controlling costs and securing storage - Traceability and Optimization 1. Optimizing data access and movement 2. Maximizing performance in demanding AI workloads 3. Ensuring traceability for code, data, and models
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
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. BasePod 2. SuperPOD 3. OVX architectures
AI Overview	15%	- Algorithm Types 1. Supervised learning 2. Unsupervised learning 3. Reinforcement learning - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - Machine Learning Fundamentals 1. Understand the relationship between AI, machine learning, and deep learning 2. Describe machine learning benefits - AI Deployment Models 1. Cloud 2. On-premises 3. Edge 4. Benefits and risks of each model - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - AI Industry Applications 1. Digital twins 2. Healthcare applications 3. Agents

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q86-Q91):

NEW QUESTION # 86
An organization has a core data center with a large AI training cluster and several remote edge locations for data ingest and local inference. The edge locations frequently need access to the latest models trained in the core data center, but WAN bandwidth is limited and can be unreliable.
Users at the edge are reporting slow model loading times.
An architect reviews the data access logs from an edge site:
Timestamp: 2025-07-11T15:30:00Z
Event: Model_Load_Request
Model_Path: nfs://core-filer.example.com/vol/models/latest_model.pkl
Source_IP: 192.168.100.15 (Edge Server)
Destination_IP: 10.1.1.50 (Core Filer)
Status: SUCCESS
Duration: 3600s (60 minutes)
What is the most likely cause of the slow model loading times at the edge?

A. The NFS version used between the core and edge is outdated.
B. The core ONTAP filer is using slow, capacity-based disks.
C. The model file is being transferred over a slow, high-latency WAN link for every load request.
D. The edge server does not have enough RAM to cache the model effectively.

Answer: C

NEW QUESTION # 87
Given the company's goal of combining physics-based simulations with AI-driven analytics on a shared data foundation, which industry trend does this project best represent?

A. The replacement of all physical testing with digital simulations.
B. The convergence of AI, High-Performance Computing (HPC), and analytics on a unified data infrastructure.
C. The exclusive use of public cloud resources for all computational tasks.
D. The separation of AI and HPC into dedicated, air-gapped environments.

Answer: B

NEW QUESTION # 88
What is the primary architectural benefit of using technologies like RDMA (Remote Direct Memory Access) and GPUDirect Storage in a high-performance AI training cluster?

A. They reduce the power consumption of the storage array by offloading checksum calculations.
B. They enable the use of lower-cost Ethernet switches in place of InfiniBand fabrics without any performance degradation.
C. They allow GPUs to communicate directly with each other and with NVMe storage, bypassing the host CPU and system memory, which significantly reduces I/O latency and CPU overhead.
D. They automatically encrypt data in-flight between the GPU servers and the storage system.

Answer: C

NEW QUESTION # 89
An architect is explaining different data storage paradigms to a new data engineering team. The team needs to understand where to store different types of data for their AI projects. Which statement accurately describes a key difference between a data lake and a data warehouse?

A. A data lake uses a schema-on-write approach, while a data warehouse uses a schema-on-read approach.
B. A data lake is designed to store vast quantities of raw, multi-format data (structured, semi- structured, and unstructured), while a data warehouse is typically used for storing structured and filtered data that has already been processed for a specific purpose.
C. A data lake stores structured, processed data for fast querying, while a data warehouse stores raw, unstructured data in its native format.
D. A data lake is only for on-premises deployments, while a data warehouse is only for cloud deployments.

Answer: B

NEW QUESTION # 90
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. A custom Python script using the NetApp DataOps Toolkit to trigger the data movement.
B. NetApp SnapMirror to replicate data from the StorageGRID object store to the ASA's file system.
C. A manual copy process using an S3 client on a bastion host.
D. NetApp BlueXP copy and sync service to create and manage the data synchronization relationship between the S3 source and the NFS/CIFS destination.
E. NetApp FabricPool to automatically tier the data from StorageGRID to the ASA.

Answer: A,D

NEW QUESTION # 91
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