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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Lifecycle	27%	- Predictive vs generative AI - AI lifecycle stages: design, training, deployment, monitoring - AI governance, ethics, and compliance - Model training, inference, and optimization - Data preparation and management for AI
Security, Reliability, and Operations	15%	- Monitoring, logging, and troubleshooting AI environments - Data security and access control for AI - High availability and data protection - Cost management and efficiency
AI Overview	15%	- 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 - AI deployment models: on-premises, cloud, edge
NetApp AI Solutions and Architecture	25%	- Data management and data pipeline design - Storage architectures for AI workloads - Scalability and performance optimization for AI - NetApp AI-ready infrastructure components - ONTAP integration with AI frameworks
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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q84-Q89):

NEW QUESTION # 84
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 StorageGRID 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 E-Series for the data lake and a NetApp ASA system for the vector database.

Answer: A

NEW QUESTION # 85
A university is building a shared AI research platform. They have two primary requirements:
1. Performance: A "hot" research area for active model training and development that requires the absolute lowest latency and highest throughput to support multiple, simultaneous GPU- intensive jobs.
The data in this area is around 50 TB.
2. Capacity & Cost: A "cold" data lake to store over 5 PB of raw, unstructured experimental data that is infrequently accessed but must be retained for compliance and future use. This tier must be as costeffective as possible.
Which combination of NetApp hardware and technologies should an architect select to build a complete, optimized, and cost-effective solution? (Select all that apply.)

A. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.
B. Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
C. Use a standard 10GbE network for all connectivity to reduce costs.
D. Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
E. Use NetApp StorageGRID to build the 5 PB cost-effective data lake.
F. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.

Answer: A,B,D,E

NEW QUESTION # 86
An internal audit requires the firm to prove the exact state of the financial product documentation that was used by the chatbot to answer a specific query from last Tuesday. The query log shows the request was processed at '2025-07-11T14:30:00Z'. The data pipeline that updates the vector database runs daily at midnight.
Which NetApp technology allows the team to instantly access an immutable, point-in-time version of the vector database volume as it existed on that specific day?

A. NetApp SnapMirror
B. NetApp FlexClone
C. NetApp FabricPool
D. NetApp Snapshots

Answer: D

NEW QUESTION # 87
Which AI technology is used to generate new, never-before-seen content such as images or text?

A. Predictive AI
B. Generative AI
C. Reinforcement AI
D. Supervised AI

Answer: B

NEW QUESTION # 88
A training job on one of the NVIDIA DGX servers is running slowly. A performance engineer runs the 'dstat' command on the server and captures the following output during the job execution.
-total-cpu-usage- -dsk/total- -net/total- paging-- system-- usr sys idl wai hiq siq| read writ| recv send| in out | int csw 15 5 70 10 0 0| 1.2G 15.0M| 1.2G 12.0M| 0 0 | 15k 35k
14 6 69 11 0 0| 1.2G 14.3M| 1.2G 11.8M| 0 0 | 14k 33k
16 5 68 11 0 0| 1.2G 16.1M| 1.2G 13.1M| 0 0 | 16k 36k
The server is connected to the NetApp ASA via a 100GbE (12.5 GB/s) network.
What is the most likely performance bottleneck based on this data?

A. The server's network interface is saturated.
B. The application is experiencing excessive memory paging.
C. The storage system (ASA) is unable to provide sufficient read throughput.
D. The server's CPU is overloaded.

Answer: A

NEW QUESTION # 89
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