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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
What is the primary difference between C-Series and A-Series storage for AI workloads?

A. A-Series provides GPU-based acceleration, while C-Series only supports CPU-based workloads.
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 is for smaller-scale AI workloads, while C-Series supports large-scale, high-performance AI applications.

Answer: B

NEW QUESTION # 61
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 block-based protocol using iSCSI.
B. A standard file-based system using NFS.
C. A parallel file system like BeeGFS or Lustre.
D. An object-based storage system using S3.

Answer: C

NEW QUESTION # 62
Which of the following applications use AI in the healthcare industry? (Choose two)

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

Answer: C,D

NEW QUESTION # 63
A robotics company is developing a control system for an autonomous warehouse drone. The drone must learn to navigate complex environments to pick up packages. The development team has created a physics-based simulation where the drone can attempt the task millions of times.
The drone receives a positive reward for successfully retrieving a package and a negative penalty for collisions. Which type of machine learning algorithm is being used in this scenario?

A. Reinforcement learning
B. Supervised learning
C. Generative learning
D. Unsupervised learning

Answer: A

NEW QUESTION # 64
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 model is too large to fit into the edge server's memory.
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 on-premises ONTAP system is not configured for high-throughput.

Answer: B

NEW QUESTION # 65
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