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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Overview	15%	- AI Industry Applications 1. Healthcare applications 2. Agents 3. Digital twins - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - Machine Learning Fundamentals 1. Describe machine learning benefits 2. Understand the relationship between AI, machine learning, and deep learning - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - Algorithm Types 1. Supervised learning 2. Reinforcement learning 3. Unsupervised learning - AI Deployment Models 1. Edge 2. Cloud 3. Benefits and risks of each model 4. On-premises
AI Hardware Architectures	18%	- Networking and Storage 1. Network protocols for AI workloads 2. Storage architectures for AI - NetApp Architectures 1. OVX architectures 2. BasePod 3. SuperPOD - Infrastructure Topologies 1. Data aggregation and compute topologies
AI Software Architectures	18%	- Development Tools 1. NetApp DataOps Toolkit 2. Jupyter notebooks vs. pipelines - Scaling and Orchestration 1. Scaling AI workloads with Kubernetes 2. Leveraging BlueXP software tools - MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale
AI Common Challenges	22%	- Traceability and Optimization 1. Ensuring traceability for code, data, and models 2. Maximizing performance in demanding AI workloads 3. Optimizing data access and movement - Resource Management 1. Controlling costs and securing storage 2. Sizing storage and compute resources effectively
AI Lifecycle	27%	- Model Development 1. Fine-tuning workflows 2. Inferencing 3. Model building - Data Preparation 1. Data aggregation and cleansing 2. XCP and CopySync 3. NetApp BlueXP Classification - Generative AI Concepts 1. Fine-tuning 2. Retrieval Augmented Generation (RAG) 3. Hallucinations - Predictive AI vs. Generative AI 1. Distinction between predictive and generative AI 2. Large Language Models (LLMs) 3. Impact of generative content (text, images, video, decision-making)

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q22-Q27):

NEW QUESTION # 22
A robotics company is developing a control system for an autonomous warehouse drone. The drone must learn to navigate complex environments to pick up packages. The development team has created a physics-based simulation where the drone can attempt the task millions of times.
The drone receives a positive reward for successfully retrieving a package and a negative penalty for collisions. Which type of machine learning algorithm is being used in this scenario?

A. Unsupervised learning
B. Generative learning
C. Supervised learning
D. Reinforcement learning

Answer: D

NEW QUESTION # 23
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 convergence of AI, High-Performance Computing (HPC), and analytics on a unified data infrastructure.
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 replacement of all physical testing with digital simulations.

Answer: A

NEW QUESTION # 24
The company decides to establish a disaster recovery (DR) site in a secondary data center for the entire Digital Twin platform. The DR plan must protect the HPC data, the AI training data, and the central data lake.
The DR requirements are:
- RPO: 4 hours for all data.
- RTO: 24 hours for the entire platform.
- Process: The failover and failback process should be as automated as possible.
Which combination of technologies provides the most comprehensive DR solution?

A. Use BlueXP backup and recovery to back up all three storage systems (ASA, E-Series, StorageGRID) to a single cloud target.
B. Use NetApp SnapMirror for the ASA, StorageGRID replication for the data lake, and rely on tape backups for the E-Series/BeeGFS data.
C. Use NetApp SnapMirror for the ASA, StorageGRID's built-in replication for the data lake, and a host- based tool like 'rsync' for the E-Series/BeeGFS data, all orchestrated via BlueXP disaster recovery.
D. Deploy a MetroCluster configuration spanning the primary and DR sites for all three storage systems.

Answer: C

NEW QUESTION # 25
Which of the following are typical requirements for inferencing in AI models? (Choose two)

A. Loading the model into memory
B. Extensive training data
C. Cloud-based inference only
D. Pre-trained models

Answer: A,D

NEW QUESTION # 26
The company's finance department mandates a cost-control strategy for the petabyte-scale StorageGRID data lake. The analysis shows that 80% of the simulation data is not accessed after
90 days but must be retained for five years for regulatory compliance. Which StorageGRID feature should the architect use to automatically reduce the long-term storage costs for this inactive data?

A. Implement a FabricPool policy to tier the data to a NetApp ASA system.
B. Use NetApp SnapMirror to replicate the cold data to a different, lower-cost StorageGRID cluster.
C. Configure an Information Lifecycle Management (ILM) policy that automatically moves objects older than 90 days to a lower-cost cloud archive tier, such as Amazon S3 Glacier Deep Archive.
D. Enable deduplication and compression on all StorageGRID storage nodes.

Answer: C

NEW QUESTION # 27
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