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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Software Architectures	18%	- Development Tools 1. Jupyter notebooks vs. pipelines 2. NetApp DataOps Toolkit - Scaling and Orchestration 1. Leveraging BlueXP software tools 2. Scaling AI workloads with Kubernetes - MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale
AI Lifecycle	27%	- Generative AI Concepts 1. Fine-tuning 2. Retrieval Augmented Generation (RAG) 3. Hallucinations - Data Preparation 1. Data aggregation and cleansing 2. XCP and CopySync 3. NetApp BlueXP Classification - Model Development 1. Fine-tuning workflows 2. Inferencing 3. Model building - Predictive AI vs. Generative AI 1. Distinction between predictive and generative AI 2. Large Language Models (LLMs) 3. Impact of generative content (text, images, video, decision-making)
AI Overview	15%	- AI Deployment Models 1. Benefits and risks of each model 2. Edge 3. On-premises 4. Cloud - 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. Healthcare applications 2. Digital twins 3. Agents - Algorithm Types 1. Supervised learning 2. Reinforcement learning 3. Unsupervised learning
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. Controlling costs and securing storage 2. Sizing storage and compute resources effectively
AI Hardware Architectures	18%	- NetApp Architectures 1. BasePod 2. OVX architectures 3. SuperPOD - Networking and Storage 1. Network protocols for AI workloads 2. Storage architectures for AI - Infrastructure Topologies 1. Data aggregation and compute topologies

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q77-Q82):

NEW QUESTION # 77
The HPC cluster generates simulation data at an extremely high rate, requiring a storage system that can handle massively parallel writes from hundreds of compute nodes simultaneously. Which storage system and file protocol combination is the most appropriate choice for the HPC cluster's high-performance scratch space?

A. A NetApp StorageGRID system accessed via the S3 protocol.
B. A Cloud Volumes ONTAP instance with a standard file system.
C. A NetApp ASA system serving a single, large NFS volume.
D. A NetApp E-Series system serving a BeeGFS parallel file system.

Answer: D

NEW QUESTION # 78
A research lab uses a fleet of autonomous drones to collect high-resolution aerial imagery for agricultural analysis. The drones land at a remote edge location and offload their data. The AI models for image analysis are trained at a central data center. The team is using NetApp SnapMirror to replicate the data from the edge to the core. However, the data scientists are complaining that the datasets arriving at the data center are often incomplete or corrupted.
An administrator reviews the SnapMirror configuration and status via the BlueXP API:
{
"source": { "workingEnvironmentId": "OnPrem-Edge-Filer-1", "volumeName": "drone_data_raw" },
"destination": { "workingEnvironmentId": "Core-Datacenter-A800", "volumeName":
"drone_data_replicated" },
"mirrorState": "broken-off",
"relationshipStatus": "idle",
"unhealthyReason": "Transfer failed. Destination volume is out of space.", "lastTransferInfo": {
"transferError": "No space left on device"
}
}
What is the direct cause of the incomplete datasets at the data center?

A. The BlueXP Connector does not have the correct permissions to manage the SnapMirror relationship.
B. The source volume at the edge location has become corrupted.
C. The network connection between the edge and the core is unreliable.
D. The SnapMirror relationship is broken because the destination volume at the core data center has run out of capacity.

Answer: D

NEW QUESTION # 79
In the data pipeline, which NetApp technology is used to create an efficient, block-level, incremental replication of the anonymized datasets from the on-premises ASA to the Cloud Volumes ONTAP instance in Azure?

A. NetApp FabricPool
B. NetApp SnapMirror
C. NetApp XCP
D. NetApp BlueXP copy and sync

Answer: B

NEW QUESTION # 80
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 GenAI toolkit requires a specific license that has not been added to the BlueXP digital wallet.
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 selected ONTAP version does not support the GenAI toolkit.

Answer: C

NEW QUESTION # 81
An AI architect is designing a solution for a legal firm. The primary goal is to allow lawyers to ask natural language questions about case law stored in a private, 50 TB document repository.
The key project constraints are as follows:
Project_Goal: Answer questions using proprietary, real-time legal documents.
Constraint_1: Must not alter the foundational LLM's weights due to compliance.
Constraint_2: Case law database is updated daily with new rulings.
Constraint_3: All generated answers must be traceable to a source document.
Which technology should the architect choose as the core of this solution?

A. A predictive AI model to classify legal documents.
B. A new LLM trained from scratch on the legal documents.
C. A Retrieval-Augmented Generation (RAG) architecture.
D. A fine-tuning pipeline to update the LLM daily.

Answer: C

NEW QUESTION # 82
......

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