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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Topic 1: Industry Use Cases	- AI applications across industries 1. Digital twins 2. Autonomous systems and agents 3. Healthcare AI
Topic 2: AI Lifecycle and Deployment	- End-to-end AI lifecycle 1. Model deployment and monitoring 2. Model training and validation 3. Data ingestion and preparation - Operational challenges 1. Data governance and management 2. Scalability and performance optimization
Topic 3: AI and Machine Learning Fundamentals	- AI, ML, DL concepts 1. Differences between AI, machine learning, and deep learning 2. Training, inference, and prediction workflows - Algorithm types 1. Supervised learning 2. Reinforcement learning 3. Unsupervised learning
Topic 4: 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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q95-Q100):

NEW QUESTION # 95
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. Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
B. Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
C. Use a standard 10GbE network for all connectivity to reduce costs.
D. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.
E. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.
F. Use NetApp StorageGRID to build the 5 PB cost-effective data lake.

Answer: A,B,E,F

NEW QUESTION # 96
Which of the following best describes the difference between data lakes, data warehouses, and lakehouses?

A. Data lakes store raw, unstructured data, data warehouses store structured data, and lakehouses combine the features of both.
B. Data lakes store data in cloud storage, data warehouses store it in traditional databases, and lakehouses store it in external drives.
C. Data lakes store structured data, data warehouses store unstructured data, and lakehouses store only real-time data.
D. Data lakes store metadata, data warehouses store transaction data, and lakehouses store archival data.

Answer: A

NEW QUESTION # 97
An architect is designing an AI solution for a European hospital chain to analyze patient diagnostic scans. The project is subject to strict GDPR regulations, which mandate that patient data cannot leave the sovereign territory. The application also requires near-instantaneous results for physicians reviewing the scans in the hospital.
Which deployment model best satisfies these security and performance requirements?

A. A multi-cloud strategy using different providers for training and inference to avoid vendor lock-in.
B. An on-premises private cloud for training combined with edge deployments in each hospital for inference.
C. A hybrid model using a public cloud for training and on-premises for inference.
D. A centralized public cloud deployment in North America for maximum scalability.

Answer: B

NEW QUESTION # 98
Given the firm's requirements for using a private, constantly updated knowledge base and the strict mandate for data traceability, which AI architecture is the most appropriate foundation for the "Advisor Assistant" chatbot?

A. A Retrieval-Augmented Generation (RAG) architecture that retrieves relevant context from a local vector database to enrich prompts sent to the LLM.
B. A standalone LLM deployed in an air-gapped environment with no access to the document repository.
C. A fine-tuning architecture where a base LLM is continuously retrained on the entire document repository.
D. A predictive AI model trained to forecast market trends.

Answer: A

NEW QUESTION # 99
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 inferencing, and Phase 2 is training.
C. Both Phase 1 and Phase 2 are examples of training.
D. Phase 1 is training, and Phase 2 is inferencing.

Answer: D

NEW QUESTION # 100
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