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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Lifecycle	27%	- Predictive AI vs. Generative AI 1. Impact of generative content (text, images, video, decision-making) 2. Distinction between predictive and generative AI 3. Large Language Models (LLMs) - Generative AI Concepts 1. Hallucinations 2. Fine-tuning 3. Retrieval Augmented Generation (RAG) - Model Development 1. Inferencing 2. Model building 3. Fine-tuning workflows - Data Preparation 1. XCP and CopySync 2. NetApp BlueXP Classification 3. Data aggregation and cleansing
Topic 2: AI Overview	15%	- Machine Learning Fundamentals 1. Describe machine learning benefits 2. Understand the relationship between AI, machine learning, and deep learning - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - Algorithm Types 1. Supervised learning 2. Unsupervised learning 3. Reinforcement learning - AI Industry Applications 1. Agents 2. Digital twins 3. Healthcare applications - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - AI Deployment Models 1. Cloud 2. Edge 3. Benefits and risks of each model 4. On-premises
Topic 3: AI Hardware Architectures	18%	- NetApp Architectures 1. OVX architectures 2. SuperPOD 3. BasePod - Networking and Storage 1. Storage architectures for AI 2. Network protocols for AI workloads - Infrastructure Topologies 1. Data aggregation and compute topologies
Topic 4: 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
Topic 5: 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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q79-Q84):

NEW QUESTION # 79
Which storage protocols are commonly used for handling large-scale AI data? (Choose two)

A. Parallel file systems
B. File-based systems
C. POSIX-based file systems
D. Object-based storage

Answer: A,D

NEW QUESTION # 80
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 FabricPool to automatically tier all raw data to Azure Blob storage, then mount the blob container to the training VMs.
B. Manually copy raw data to Azure, run a cloud-based script to anonymize it, then start training.
C. Use SnapMirror to replicate the raw, sensitive data to Azure first, then run BlueXP classification in the cloud to find PII.
D. 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.

Answer: D

NEW QUESTION # 81
Which of the following applications use AI in the healthcare industry? (Choose two)

A. Diagnosing diseases
B. Predicting patient outcomes
C. Managing hospital supply chains
D. Automating financial reporting

Answer: A,B

NEW QUESTION # 82
A media company is building a new generative AI service. The project has two main components:
1. Data Lake & Fine-Tuning: A 300 TB repository of unstructured data (videos, images, text) stored as objects will be used to fine-tune a foundational model. This process requires a scalable, cost-effective storage solution that can integrate with cloud-native data processing tools like Apache Spark.
2. Inference & RAG: The fine-tuned model will be used in a customer-facing application that leverages Retrieval-Augmented Generation (RAG). To ensure low-latency responses, the RAG component requires extremely fast lookups from a 10 TB vector database.
The company needs a solution that optimizes both cost and performance for this entire lifecycle.
Which combination of NetApp technologies provides the most appropriate solution for this scenario?

A. Use NetApp E-Series for the data lake and a NetApp ASA system for the vector database.
B. Use a single, large NetApp ASA system for both the object data lake and the vector database.
C. Use NetApp Cloud Volumes ONTAP for the data lake and NetApp StorageGRID for the vector database.
D. Use NetApp StorageGRID for the data lake and a NetApp ASA system for the vector database.

Answer: D

NEW QUESTION # 83
An online retail company's recommendation engine, which provides real-time product suggestions to users, is experiencing unacceptable latency. The inference application is running on a correctly-sized edge server, but user requests are taking over 500ms to process. An architect reviews the data access pattern and infrastructure diagram.
Application_Location: Edge Server (In-store)
Data_Source_Location: Core Data Center (On-premises ONTAP)
Data_Required_for_Inference: User profile data, product catalog vectors Network_Path: Edge -> WAN -> Core Data Center Observed_Latency: 550ms What is the most likely cause of the high inference latency?

A. The on-premises ONTAP system is not configured for high-throughput.
B. Every inference request requires a high-latency round trip over the WAN to fetch data from the core data center.
C. The edge server has insufficient CPU resources to run the model.
D. The model is too large to fit into the edge server's memory.

Answer: B

NEW QUESTION # 84
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