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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Common Challenges	22%	- Traceability and Optimization 1. Maximizing performance in demanding AI workloads 2. Ensuring traceability for code, data, and models 3. Optimizing data access and movement - Resource Management 1. Controlling costs and securing storage 2. Sizing storage and compute resources effectively
AI Software Architectures	18%	- Scaling and Orchestration 1. Scaling AI workloads with Kubernetes 2. Leveraging BlueXP software tools - 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
AI Lifecycle	27%	- Generative AI Concepts 1. Fine-tuning 2. Retrieval Augmented Generation (RAG) 3. Hallucinations - Model Development 1. Model building 2. Fine-tuning workflows 3. Inferencing - Data Preparation 1. NetApp BlueXP Classification 2. Data aggregation and cleansing 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
AI Hardware Architectures	18%	- NetApp Architectures 1. BasePod 2. OVX architectures 3. SuperPOD - Infrastructure Topologies 1. Data aggregation and compute topologies - Networking and Storage 1. Storage architectures for AI 2. Network protocols for AI workloads
AI Overview	15%	- AI Deployment Models 1. Edge 2. On-premises 3. Cloud 4. Benefits and risks of each model - 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 - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - AI Industry Applications 1. Digital twins 2. Healthcare applications 3. Agents - Algorithm Types 1. Reinforcement learning 2. Unsupervised learning 3. Supervised learning

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q27-Q32):

NEW QUESTION # 27
An organization is planning to deploy a large AI infrastructure but wants to avoid a large, upfront capital expenditure. They prefer an operational expenditure (OpEx) model where they pay for storage and compute resources as they are consumed. They also need the flexibility to scale resources up or down based on project demands.
Which NetApp consumption model is specifically designed to meet these financial and operational requirements?

A. NetApp Keystone, which provides a subscription-based, pay-as-you-go service for on-premises and cloud storage.
B. NetApp Cloud Volumes ONTAP PAYGO licensing only.
C. A perpetual licensing model for all ONTAP software features.
D. A standard capital purchase of AFF and ASA systems.

Answer: A

NEW QUESTION # 28
An AI infrastructure architect is tasked with designing a solution to address two critical challenges in a large, multi-petabyte AI environment:
1. Cost: A significant portion of the data on the high-performance all-flash storage is inactive but must remain online. The cost of storing this cold data on the performance tier is prohibitive.
2. Traceability: Data scientists need a simple, space-efficient way to version their datasets at key points in their workflow to ensure reproducibility.
The environment consists of NetApp AFF A-Series and NetApp StorageGRID systems.
Which combination of NetApp technologies should the architect implement to solve both challenges simultaneously? (Select all that apply.)

A. Implement NetApp FabricPool to automatically tier cold data blocks from the AFF systems to StorageGRID.
B. Use BlueXP backup and recovery to create backups on StorageGRID, then delete the original volumes from the AFF systems.
C. Use NetApp XCP to periodically move cold data from the AFF systems to StorageGRID.
D. Implement NetApp FlexClone to create full, writable copies of datasets for each experiment.
E. Use NetApp SnapMirror to replicate volumes from the AFF systems to StorageGRID for archival.
F. Train data scientists to use NetApp Snapshots to create point-in-time, read-only versions of their data volumes.

Answer: A,F

NEW QUESTION # 29
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 standard file-based system using NFS.
B. A parallel file system like BeeGFS or Lustre.
C. A block-based protocol using iSCSI.
D. An object-based storage system using S3.

Answer: B

NEW QUESTION # 30
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. Purchase an additional, larger AFF A-Series system to gain better storage efficiency through deduplication.
B. Implement NetApp FabricPool to automatically tier the inactive data blocks from the AFF A-Series to the StorageGRID system.
C. Use NetApp SnapMirror to replicate the entire 200 TB volume to the StorageGRID system and then delete the source.
D. Manually identify and delete the 150 TB of inactive data from the AFF A-Series.

Answer: B

NEW QUESTION # 31
The data science team in Azure reports that training jobs are taking longer than expected. An analysis of the Cloud Volumes ONTAP instance in Azure shows that the instance type is undersized for the I/O demands of the training workload. The architect needs to change the Azure VM instance type for the Cloud Volumes ONTAP system to a more powerful one.
The current configuration is:
Cloud_Provider: Azure
ONTAP_System: Cloud Volumes ONTAP (Single Node)
Current_Instance_Type: Standard_DS3_v2
Target_Instance_Type: Standard_E8s_v4
What is the most direct method to perform this operation using NetApp's management tools?

A. Use the Azure portal to manually change the VM instance type.
B. Terminate the existing Cloud Volumes ONTAP instance and deploy a new one with the target instance type, then restore data from backup.
C. SSH to the Cloud Volumes ONTAP instance and run an ONTAP command to modify the underlying VM.
D. From the BlueXP Canvas, select the Cloud Volumes ONTAP working environment and use the
"Change Instance Type" action in the Features panel.

Answer: D

NEW QUESTION # 32
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