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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q69-Q74):

NEW QUESTION # 69
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 FabricPool
B. NetApp StorageGRID
C. NetApp FlexCache
D. NetApp XCP
E. NetApp SnapMirror

Answer: A,B

NEW QUESTION # 70
Which of the following describes the impact of generative AI in content creation?

A. Generative AI analyzes data without generating content.
B. Generative AI can create new content such as text, images, and videos.
C. Generative AI only processes pre-existing content.
D. Generative AI predicts customer preferences without creating new content.

Answer: B

NEW QUESTION # 71
A data scientist on the team wants to run an experiment on a new vector indexing strategy. To do this, they need a temporary, writable copy of the main vector database. They use the NetApp DataOps Toolkit for Python to automate this process.
The following script is executed:
from netapp_dataops.k8s import clone_pvc
clone_pvc(
source_pvc_name="prod-vector-db-pvc",
new_pvc_name="exp-indexing-clone",
namespace="ds-prod"
)
Assuming the source PVC exists and the user has correct permissions, what is the primary benefit of using this method?

A. It creates a read-only Snapshot, ensuring the integrity of the experimental data.
B. It creates a full physical copy of the data, providing the best performance isolation.
C. It automatically migrates the data from the AFF A-Series to the StorageGRID system for the experiment.
D. It uses NetApp FlexClone technology to create a near-instantaneous, space-efficient writable copy of the volume, accelerating the experimental workflow.

Answer: D

NEW QUESTION # 72
Given the company's goal of combining physics-based simulations with AI-driven analytics on a shared data foundation, which industry trend does this project best represent?

A. The replacement of all physical testing with digital simulations.
B. The exclusive use of public cloud resources for all computational tasks.
C. The separation of AI and HPC into dedicated, air-gapped environments.
D. The convergence of AI, High-Performance Computing (HPC), and analytics on a unified data infrastructure.

Answer: D

NEW QUESTION # 73
An AI team is embarking on a project to train a new, large-scale computer vision model from scratch. The lead architect emphasizes that the success of the project depends on four fundamental inputs that must be available and managed throughout the training process. Which of the following are the four essential requirements for model generation?

A. A pre-trained model, a validation set, an inference engine, and a cloud provider.
B. Data, code, compute, and time.
C. A project manager, a data scientist, a software engineer, and a budget.
D. A data lake, a data warehouse, a data pipeline, and a data mart.

Answer: B

NEW QUESTION # 74
......

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