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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Lifecycle	27%	- Model Development 1. Fine-tuning workflows 2. Inferencing 3. Model building - Data Preparation 1. Data aggregation and cleansing 2. NetApp BlueXP Classification 3. XCP and CopySync - 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) - Generative AI Concepts 1. Fine-tuning 2. Hallucinations 3. Retrieval Augmented Generation (RAG)
Topic 2: AI Software Architectures	18%	- MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale - Scaling and Orchestration 1. Scaling AI workloads with Kubernetes 2. Leveraging BlueXP software tools - Development Tools 1. NetApp DataOps Toolkit 2. Jupyter notebooks vs. pipelines
Topic 3: AI Overview	15%	- Machine Learning Fundamentals 1. Describe machine learning benefits 2. Understand the relationship between AI, machine learning, and deep learning - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - AI Industry Applications 1. Agents 2. Healthcare applications 3. Digital twins - AI Deployment Models 1. Benefits and risks of each model 2. Cloud 3. On-premises 4. Edge - Algorithm Types 1. Reinforcement learning 2. Supervised learning 3. Unsupervised learning - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics
Topic 4: AI Hardware Architectures	18%	- Infrastructure Topologies 1. Data aggregation and compute topologies - Networking and Storage 1. Storage architectures for AI 2. Network protocols for AI workloads - NetApp Architectures 1. BasePod 2. SuperPOD 3. OVX architectures
Topic 5: AI Common Challenges	22%	- Traceability and Optimization 1. Optimizing data access and movement 2. Ensuring traceability for code, data, and models 3. Maximizing performance in demanding AI workloads - Resource Management 1. Controlling costs and securing storage 2. Sizing storage and compute resources effectively

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q29-Q34):

NEW QUESTION # 29
An automotive company runs crash simulations on a dedicated High-Performance Computing (HPC) cluster and trains computer vision models on a separate AI cluster. Data scientists are complaining about the long delays required to move terabytes of simulation output data from the HPC storage to the AI cluster's storage before they can begin training.
The current data flow is as follows:
HPC Cluster -> --Manual Copy (NFS)--> -> AI Cluster
An architect has been asked to redesign the infrastructure to eliminate this data movement bottleneck.
Which architectural change would be most effective?

A. Install faster CPUs in the AI cluster's storage controllers.
B. Use NetApp XCP to perform the data copy, as it is faster than a standard NFS copy.
C. Upgrade the network connection between the two storage systems to 200GbE.
D. Implement a converged data infrastructure where both the HPC and AI clusters access a single, high- performance data lake built on NetApp storage.

Answer: D

NEW QUESTION # 30
An AI architect is planning the resource allocation for a new project. The primary task is to process millions of unlabeled customer reviews to identify naturally occurring groups or themes without any prior guidance.
The project requirements are summarized below:
Task: Discover hidden patterns in text data
Input_Data: 10 million unlabeled text reviews
Output: Clustered groups of related reviews
Supervision: None
Which type of machine learning algorithm is required for this task?

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

Answer: D

NEW QUESTION # 31
What is the primary difference between C-Series and A-Series storage for AI workloads?

A. C-Series is designed for high-performance workloads, while A-Series focuses on cost-efficiency for general storage.
B. A-Series provides GPU-based acceleration, while C-Series only supports CPU-based workloads.
C. A-Series is for smaller-scale AI workloads, while C-Series supports large-scale, high-performance AI applications.
D. C-Series supports both file and object-based storage, while A-Series is optimized for object storage.

Answer: A

NEW QUESTION # 32
An AI architect is designing a storage solution for a new training cluster. The primary workload consists of training large language models, which involves sequential reads of massive datasets.
The key requirement is to maximize GPU utilization by providing the highest possible data throughput. Cost is a secondary concern to performance.
Which NetApp storage system is the most appropriate choice for this workload?

A. NetApp StorageGRID
B. NetApp ASA (All-SAN Array)
C. NetApp C-Series
D. NetApp E-Series

Answer: B

NEW QUESTION # 33
An architect is designing the storage and network infrastructure for a new, large-scale AI cluster dedicated to training foundational models. The primary design goal is to achieve the highest possible data throughput and the lowest latency to ensure multi-million dollar GPU resources are never idle. Which two technologies are essential to include in the design to achieve this goal?
(Choose 2.)

A. A 10GbE Ethernet network for all data traffic.
B. A tiered storage architecture using NetApp FabricPool.
C. An InfiniBand or RoCE-capable Ethernet network fabric.
D. NetApp StorageGRID as the primary storage for the training datasets.
E. GPUDirect Storage support on the storage system.

Answer: C,E

NEW QUESTION # 34
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