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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Industry Use Cases	- AI applications across industries 1. Healthcare AI 2. Digital twins 3. Autonomous systems and agents
AI Infrastructure and NetApp Solutions	- AI-ready data infrastructure 1. High-performance storage for AI workloads 2. Data pipeline optimization for AI/ML workloads - Converged workloads 1. Shared infrastructure design considerations 2. AI, HPC, and analytics convergence
AI and Machine Learning Fundamentals	- Algorithm types 1. Unsupervised learning 2. Supervised learning 3. Reinforcement learning - AI, ML, DL concepts 1. Differences between AI, machine learning, and deep learning 2. Training, inference, and prediction workflows
AI Lifecycle and Deployment	- Operational challenges 1. Scalability and performance optimization 2. Data governance and management - End-to-end AI lifecycle 1. Model deployment and monitoring 2. Model training and validation 3. Data ingestion and preparation

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
An organization is planning to deploy a large AI infrastructure but wants to avoid a large, upfront capital expenditure. They prefer an operational expenditure (OpEx) model where they pay for storage and compute resources as they are consumed. They also need the flexibility to scale resources up or down based on project demands.
Which NetApp consumption model is specifically designed to meet these financial and operational requirements?

A. NetApp Cloud Volumes ONTAP PAYGO licensing only.
B. A standard capital purchase of AFF and ASA systems.
C. A perpetual licensing model for all ONTAP software features.
D. NetApp Keystone, which provides a subscription-based, pay-as-you-go service for on-premises and cloud storage.

Answer: D

NEW QUESTION # 24
A research institute is designing an infrastructure to support its entire AI drug discovery pipeline.
The pipeline has two distinct workload requirements:
1. Training: A team of data scientists needs to train several large transformer models simultaneously using a 500 TB dataset of genomic sequences. This process requires maximum data throughput to keep the GPUs saturated.
2. Inference: Once trained, the models are deployed to an internal web portal where researchers submit individual protein sequences for analysis. These queries must return results with the lowest possible latency.
Which infrastructure design best satisfies both requirements? (Choose 2.)

A. Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.
B. Use a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.
C. Configure QoS minimums on the training volumes to ensure they do not impact inference performance.
D. Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.
E. Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.

Answer: D,E

NEW QUESTION # 25
An automotive company runs crash simulations on a dedicated High-Performance Computing (HPC) cluster and trains computer vision models on a separate AI cluster. Data scientists are complaining about the long delays required to move terabytes of simulation output data from the HPC storage to the AI cluster's storage before they can begin training.
The current data flow is as follows:
HPC Cluster -> --Manual Copy (NFS)--> -> AI Cluster
An architect has been asked to redesign the infrastructure to eliminate this data movement bottleneck.
Which architectural change would be most effective?

A. Upgrade the network connection between the two storage systems to 200GbE.
B. Use NetApp XCP to perform the data copy, as it is faster than a standard NFS copy.
C. Install faster CPUs in the AI cluster's storage controllers.
D. Implement a converged data infrastructure where both the HPC and AI clusters access a single, high- performance data lake built on NetApp storage.

Answer: D

NEW QUESTION # 26
A company is running its AI training workloads on a NetApp AFF A-Series system. To manage costs, they want to automatically move inactive training datasets and older model checkpoints from the high- performance all-flash tier to a lower-cost object storage tier, such as an on- premises StorageGRID or a public cloud bucket. The process must be transparent to the data scientists and not require changes to their scripts or file paths.
Which two NetApp technologies should be combined to achieve this goal? (Choose 2.)

A. NetApp StorageGRID
B. NetApp FabricPool
C. NetApp SnapMirror
D. NetApp FlexCache
E. NetApp XCP

Answer: A,B

NEW QUESTION # 27
An organization is developing a new AI-powered application. The initial phase involves feeding a curated 50 TB dataset of labeled images into a complex neural network, allowing the model to learn and adjust its internal parameters over millions of iterations. The second phase involves deploying this finalized model to a web service where it will process single, user-uploaded images and return a classification in real-time.
Which statement accurately describes these two phases?

A. Both Phase 1 and Phase 2 are examples of inferencing.
B. Phase 1 is training, and Phase 2 is inferencing.
C. Both Phase 1 and Phase 2 are examples of training.
D. Phase 1 is inferencing, and Phase 2 is training.

Answer: B

NEW QUESTION # 28
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