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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Overview	15%	- 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 - Algorithm Types 1. Unsupervised learning 2. Reinforcement learning 3. Supervised learning - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - AI Deployment Models 1. On-premises 2. Benefits and risks of each model 3. Cloud 4. Edge - AI Industry Applications 1. Healthcare applications 2. Digital twins 3. Agents
Topic 2: AI Common Challenges	22%	- Traceability and Optimization 1. Ensuring traceability for code, data, and models 2. Optimizing data access and movement 3. Maximizing performance in demanding AI workloads - Resource Management 1. Controlling costs and securing storage 2. Sizing storage and compute resources effectively
Topic 3: AI Software Architectures	18%	- Scaling and Orchestration 1. Scaling AI workloads with Kubernetes 2. Leveraging BlueXP software tools - Development Tools 1. Jupyter notebooks vs. pipelines 2. NetApp DataOps Toolkit - MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale
Topic 4: AI Hardware Architectures	18%	- NetApp Architectures 1. OVX architectures 2. SuperPOD 3. BasePod - Infrastructure Topologies 1. Data aggregation and compute topologies - Networking and Storage 1. Network protocols for AI workloads 2. Storage architectures for AI
Topic 5: AI Lifecycle	27%	- 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. Hallucinations 2. Retrieval Augmented Generation (RAG) 3. Fine-tuning - Data Preparation 1. NetApp BlueXP Classification 2. XCP and CopySync 3. Data aggregation and cleansing - Model Development 1. Model building 2. Inferencing 3. Fine-tuning workflows

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q44-Q49):

NEW QUESTION # 44
The architect is designing the complete, automated data pipeline from the on-premises data center to the Azure cloud for this medical imaging project. The design must prioritize security, efficiency, and reproducibility.
Which sequence of actions provides the most robust and automated solution?

A. Use BlueXP classification to scan the on-prem data; use a DataOps Toolkit script to trigger a FlexClone of the source volume for anonymization; use SnapMirror to replicate the anonymized clone to Cloud Volumes ONTAP; then begin training in Azure.
B. Manually copy raw data to Azure, run a cloud-based script to anonymize it, then start training.
C. Use FabricPool to automatically tier all raw data to Azure Blob storage, then mount the blob container to the training VMs.
D. Use SnapMirror to replicate the raw, sensitive data to Azure first, then run BlueXP classification in the cloud to find PII.

Answer: A

NEW QUESTION # 45
An AI team is embarking on a project to train a new, large-scale computer vision model from scratch. The lead architect emphasizes that the success of the project depends on four fundamental inputs that must be available and managed throughout the training process. Which of the following are the four essential requirements for model generation?

A. A project manager, a data scientist, a software engineer, and a budget.
B. A pre-trained model, a validation set, an inference engine, and a cloud provider.
C. A data lake, a data warehouse, a data pipeline, and a data mart.
D. Data, code, compute, and time.

Answer: D

NEW QUESTION # 46
Which of the following describes the impact of generative AI in content creation?

A. Generative AI can create new content such as text, images, and videos.
B. Generative AI only processes pre-existing content.
C. Generative AI analyzes data without generating content.
D. Generative AI predicts customer preferences without creating new content.

Answer: A

NEW QUESTION # 47
An AI platform administrator is trying to deploy a new GenAI toolkit using the BlueXP Workload Factory. The deployment fails, and the administrator examines the API response from BlueXP.
{
"jobId": "we-deploy-genai-987zy",
"status": "FAILED",
"statusCode": 403,
"message":
"Forbidden: The service principal or user account used by the Connector does not have the required permissions on the target subscription to create a new resource group.
Required permission: 'Microsoft.Resources/subscriptions/resourcegroups/write'." } Based on this API response, what is the root cause of the deployment failure?

A. The selected ONTAP version does not support the GenAI toolkit.
B. The BlueXP Connector is offline and cannot communicate with the cloud provider.
C. The Azure service principal associated with the BlueXP Connector lacks the necessary IAM role to create resource groups.
D. The GenAI toolkit requires a specific license that has not been added to the BlueXP digital wallet.

Answer: C

NEW QUESTION # 48
An architect is designing a global infrastructure for a company that develops AI for autonomous vehicles.
The design must accommodate three distinct locations and functions:
1. Edge (Test Tracks): Fleets of test cars generate 100s of TBs of sensor data per day. This data must be ingested locally with high performance.
2. Core (Primary Data Center): The raw data from all edge sites must be aggregated here. This location houses the primary data lake and the main GPU cluster for large-scale model training.
3. Cloud (Public Cloud Provider): Data scientists want to use cloud-native tools for experimental data processing and model development. They also need a cost-effective location for long-term archiving of raw data.
Which combination of deployment locations and NetApp technologies creates the most logical and efficient end-to-end solution?

A. Use Cloud Volumes ONTAP at the edge, NetApp StorageGRID at the core, and on-premises ONTAP for cloud archive.
B. Deploy a single, global NetApp StorageGRID across all three locations to act as a unified data plane.
C. Use NetApp E-Series at the edge, NetApp ASA at the core, and NetApp StorageGRID in the cloud.
Use SnapMirror to move data between all three tiers.
D. Use NetApp ONTAP systems at the edge and core, and Cloud Volumes ONTAP in the public cloud.
Use SnapMirror to replicate data from edge to core, and FabricPool to tier data from the core to the cloud.

Answer: D

NEW QUESTION # 49
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