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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Overview	15%	- AI deployment models: on-premises, cloud, edge - Convergence of AI, high-performance computing, and analytics - Algorithm types: supervised, unsupervised, reinforcement learning - AI, machine learning, and deep learning concepts - AI industry use cases and applications
Topic 2: NetApp AI Solutions and Architecture	25%	- ONTAP integration with AI frameworks - Data management and data pipeline design - Storage architectures for AI workloads - Scalability and performance optimization for AI - NetApp AI-ready infrastructure components
Topic 3: Security, Reliability, and Operations	15%	- Cost management and efficiency - Data security and access control for AI - Monitoring, logging, and troubleshooting AI environments - High availability and data protection
Topic 4: AI Lifecycle	27%	- Predictive vs generative AI - Model training, inference, and optimization - Data preparation and management for AI - AI governance, ethics, and compliance - AI lifecycle stages: design, training, deployment, monitoring
Topic 5: Cloud and Hybrid Cloud AI Deployment	18%	- Data mobility and consistency across environments - Hybrid and multi-cloud AI architectures - Cloud-native AI solutions and integration - NetApp cloud data services for AI

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q87-Q92):

NEW QUESTION # 87
What is the primary difference between C-Series and A-Series storage for AI workloads?

A. A-Series is for smaller-scale AI workloads, while C-Series supports large-scale, high-performance AI applications.
B. C-Series is designed for high-performance workloads, while A-Series focuses on cost-efficiency for general storage.
C. C-Series supports both file and object-based storage, while A-Series is optimized for object storage.
D. A-Series provides GPU-based acceleration, while C-Series only supports CPU-based workloads.

Answer: B

NEW QUESTION # 88
Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)

A. RunAI
B. KubeFlow
C. TensorFlow Extended
D. Google VertexAI

Answer: A,B

NEW QUESTION # 89
A pharmaceutical company is creating a "digital twin" of its manufacturing process. This involves running complex simulations (an HPC workload) that generate massive datasets.
The company wants to use this data immediately for two other purposes:
1. Analytics: Business analysts need to run complex queries on the simulation output using tools like Spark.
2. AI Training: Data scientists need to use the same output as a training set for a predictive maintenance model.
The company wants to avoid creating separate data silos for each workload.
Which two NetApp technologies are best suited for building a unified data lake that can efficiently serve all three workloads (HPC, Analytics, AI)? (Choose 2.)

A. NetApp Keystone to provide a flexible, pay-as-you-go consumption model for the infrastructure.
B. NetApp StorageGRID to provide a scalable, S3-native object store that integrates directly with modern analytics platforms like Spark.
C. NetApp ONTAP with FlexGroup volumes to provide high-throughput, parallel NFS access for the HPC and AI training workloads.
D. NetApp SnapCenter to create application-consistent backups of the data.
E. NetApp Autonomous Ransomware Protection to secure the data from modification.

Answer: B,C

NEW QUESTION # 90
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 StorageGRID for the data lake and a NetApp ASA system for the vector database.
D. Use NetApp Cloud Volumes ONTAP for the data lake and NetApp StorageGRID for the vector database.

Answer: C

NEW QUESTION # 91
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. Train data scientists to use NetApp Snapshots to create point-in-time, read-only versions of their data volumes.
B. Use NetApp SnapMirror to replicate volumes from the AFF systems to StorageGRID for archival.
C. Implement NetApp FlexClone to create full, writable copies of datasets for each experiment.
D. Use BlueXP backup and recovery to create backups on StorageGRID, then delete the original volumes from the AFF systems.
E. Implement NetApp FabricPool to automatically tier cold data blocks from the AFF systems to StorageGRID.
F. Use NetApp XCP to periodically move cold data from the AFF systems to StorageGRID.

Answer: A,E

NEW QUESTION # 92
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