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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
Which of the following techniques are used to maximize GPU utilization in AI workloads? (Choose two)

A. Scheduling GPU-intensive tasks during off-peak hours
B. Balancing compute and storage workloads efficiently
C. Utilizing CPU-based workloads for all tasks
D. Using larger batch sizes

Answer: A,B

NEW QUESTION # 17
Due to the success of the "Advisor Assistant," the number of concurrent users is expected to double in the next quarter. The existing Kubernetes cluster is running at 80% of its GPU capacity during peak hours. The architect must propose a plan to scale the compute infrastructure to handle the increased load.
Which two strategies represent the most effective and scalable solutions? (Choose 2.)

A. Horizontally scale the cluster by adding new GPU-equipped nodes.
B. Vertically scale the existing nodes by adding more RAM and CPU cores to each one.
C. Manually deploy all new application replicas to a single, new, very large "super-node".
D. Replace all existing GPUs with the next-generation model to increase the performance of each node.
E. Configure the Kubernetes Cluster Autoscaler to automatically add new nodes to the cluster from a predefined node pool when GPU demand exceeds current capacity.

Answer: A,E

NEW QUESTION # 18
A data scientist needs to test a new data normalization technique. To do this, they require an isolated, writable copy of a 50 TB curated simulation dataset that resides on the NetApp ASA system. The operation must be completed as quickly as possible and consume minimal additional storage space. Which NetApp technology is the most appropriate solution for this requirement?

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

Answer: B

NEW QUESTION # 19
The HPC cluster generates simulation data at an extremely high rate, requiring a storage system that can handle massively parallel writes from hundreds of compute nodes simultaneously. Which storage system and file protocol combination is the most appropriate choice for the HPC cluster's high-performance scratch space?

A. A NetApp ASA system serving a single, large NFS volume.
B. A NetApp E-Series system serving a BeeGFS parallel file system.
C. A Cloud Volumes ONTAP instance with a standard file system.
D. A NetApp StorageGRID system accessed via the S3 protocol.

Answer: B

NEW QUESTION # 20
An architect is designing a comprehensive AI platform for a large enterprise. The platform must support the entire data lifecycle, from ingest at the edge to a central data lake, and finally to a high- performance training cluster.
The requirements are:
- Edge Ingest: Data must be collected at remote sites and efficiently replicated to the core.
- Data Lake: A central, petabyte-scale repository for unstructured data, accessible via the S3 protocol.
- Training Cluster: A high-performance compute cluster that requires low-latency, parallel file access to training datasets.
- Data Traceability: All datasets used for training must be immutably versioned.
Which combination of NetApp technologies and protocols should the architect choose to build this solution? (Select all that apply.)

A. Use iSCSI as the primary protocol for the data lake to ensure maximum compatibility.
B. Use NetApp Snapshots on the training dataset volumes to create immutable, point-in-time versions for traceability.
C. Use NetApp ONTAP systems at the edge and NetApp SnapMirror to replicate data to the core data center.
D. Use NetApp FlexCache to tier cold data from the data lake to the public cloud.
E. Use NetApp StorageGRID to create the petabyte-scale, S3-accessible data lake at the core.
F. Use a NetApp E-Series system with a parallel file system (like BeeGFS) to provide high- performance, parallel file access for the training cluster.

Answer: B,C,E,F

NEW QUESTION # 21
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