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Network Appliance NS0-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Security, Reliability, and Operations15%- High availability and data protection
- Monitoring, logging, and troubleshooting AI environments
- Cost management and efficiency
- Data security and access control for AI
Topic 2: NetApp AI Solutions and Architecture25%- ONTAP integration with AI frameworks
- NetApp AI-ready infrastructure components
- Data management and data pipeline design
- Storage architectures for AI workloads
- Scalability and performance optimization for AI
Topic 3: AI Lifecycle27%- Model training, inference, and optimization
- AI governance, ethics, and compliance
- Data preparation and management for AI
- AI lifecycle stages: design, training, deployment, monitoring
- Predictive vs generative AI
Topic 4: Cloud and Hybrid Cloud AI Deployment18%- Data mobility and consistency across environments
- Cloud-native AI solutions and integration
- NetApp cloud data services for AI
- Hybrid and multi-cloud AI architectures
Topic 5: AI Overview15%- Convergence of AI, high-performance computing, and analytics
- AI, machine learning, and deep learning concepts
- AI deployment models: on-premises, cloud, edge
- AI industry use cases and applications
- Algorithm types: supervised, unsupervised, reinforcement learning

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
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?

Answer: D


NEW QUESTION # 72
A healthcare organization plans to use a large dataset of patient records to train a predictive model. Before training, they must identify and segregate all records containing Personally Identifiable Information (PII) to comply with privacy regulations. The data resides on an on- premises NetApp ONTAP cluster. The organization needs an automated tool to scan the data in- place and tag files containing PII without moving the data.
The project requirements are as follows:
Task: Identify PII in a large dataset.
Data_Location: On-premises ONTAP cluster.
Constraint: Data must not be moved from its source location for scanning.
Output: Tagged files containing PII.
Which NetApp tool is designed for this specific task?

Answer: D


NEW QUESTION # 73
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.)

Answer: A,C,D,F


NEW QUESTION # 74
An AI architect is designing a solution for a legal firm. The primary goal is to allow lawyers to ask natural language questions about case law stored in a private, 50 TB document repository.
The key project constraints are as follows:
Project_Goal: Answer questions using proprietary, real-time legal documents.
Constraint_1: Must not alter the foundational LLM's weights due to compliance.
Constraint_2: Case law database is updated daily with new rulings.
Constraint_3: All generated answers must be traceable to a source document.
Which technology should the architect choose as the core of this solution?

Answer: B


NEW QUESTION # 75
The company's compliance officer mandates that all raw simulation data in the StorageGRID data lake must be protected from modification or deletion for a period of seven years. Which two StorageGRID features should be implemented to meet this strict, long-term data immutability requirement? (Choose 2.)

Answer: A,C


NEW QUESTION # 76
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