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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Software Architectures	18%	- 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 - Scaling and Orchestration 1. Scaling AI workloads with Kubernetes 2. Leveraging BlueXP software tools
AI Lifecycle	27%	- Generative AI Concepts 1. Hallucinations 2. Fine-tuning 3. Retrieval Augmented Generation (RAG) - Model Development 1. Model building 2. Inferencing 3. Fine-tuning workflows - Data Preparation 1. NetApp BlueXP Classification 2. XCP and CopySync 3. Data aggregation and cleansing - Predictive AI vs. Generative AI 1. Distinction between predictive and generative AI 2. Impact of generative content (text, images, video, decision-making) 3. Large Language Models (LLMs)
AI Hardware Architectures	18%	- Networking and Storage 1. Network protocols for AI workloads 2. Storage architectures for AI - NetApp Architectures 1. BasePod 2. OVX architectures 3. SuperPOD - Infrastructure Topologies 1. Data aggregation and compute topologies
AI Overview	15%	- AI Industry Applications 1. Digital twins 2. Healthcare applications 3. Agents - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - AI Deployment Models 1. On-premises 2. Edge 3. Benefits and risks of each model 4. Cloud - Algorithm Types 1. Reinforcement learning 2. Unsupervised learning 3. Supervised learning - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - Machine Learning Fundamentals 1. Describe machine learning benefits 2. Understand the relationship between AI, machine learning, and deep learning
AI Common Challenges	22%	- Traceability and Optimization 1. Maximizing performance in demanding AI workloads 2. Optimizing data access and movement 3. Ensuring traceability for code, data, and models - 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 (Q59-Q64):

NEW QUESTION # 59
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. Both Phase 1 and Phase 2 are examples of training.
C. Phase 1 is training, and Phase 2 is inferencing.
D. Phase 1 is inferencing, and Phase 2 is training.

Answer: C

NEW QUESTION # 60
An architect is designing a scalable, automated MLOps platform using Kubeflow on a Kubernetes cluster. The platform must support the entire AI lifecycle for multiple teams, with different storage requirements at each stage.
The key requirements are:
- Data Ingestion: A pipeline step needs a shared, read-write volume accessible by multiple pods to stage raw data.
- Experimentation: Data scientists need individual, isolated volumes for their Jupyter notebooks.
- Training: Distributed training jobs require a high-performance, parallel-access filesystem for reading training data.
- Automation: All storage must be provisioned automatically via Kubeflow pipeline definitions without manual intervention.
Which combination of technologies and configurations would create the most effective solution?

A. Use the NetApp DataOps Toolkit for all storage provisioning, bypassing Trident and Kubernetes PVCs.
B. Use the NetApp DataOps Toolkit for Python within the Kubeflow pipeline components to dynamically create and manage Trident PVCs for each stage.
C. Create a single, large NFS volume and mount it to all pods using a static PersistentVolume.
D. Configure multiple Trident backends (e.g., 'ontap-nas' for standard volumes, 'ontap-nas-flexgroup' for parallel access) and corresponding StorageClasses.
E. Rely on hostPath volumes for all storage to ensure the highest performance.

Answer: B,D

NEW QUESTION # 61
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 tiered storage architecture using NetApp FabricPool.
B. GPUDirect Storage support on the storage system.
C. NetApp StorageGRID as the primary storage for the training datasets.
D. An InfiniBand or RoCE-capable Ethernet network fabric.
E. A 10GbE Ethernet network for all data traffic.

Answer: B,D

NEW QUESTION # 62
The firm decides to expand the "Advisor Assistant" project to a new team in a different department. This team needs its own isolated environment. An MLOps engineer attempts to submit a new GPU- intensive job for the new team, but it remains pending. The engineer checks the Run:AI scheduler logs and finds the following entry:
time="2025-07-11T16:45:00Z" level=info msg="Job ds-new-team-job1 cannot be scheduled.
Project 'new-team-project' has exceeded its GPU quota. Quota: 0, Requested: 1, Used: 0." What is the root cause of the scheduling failure?

A. The job is requesting a specific type of GPU that is not available in the cluster.
B. The Run:AI scheduler is offline and cannot process new jobs.
C. A Run:AI project quota has been configured for the new team, but it has been set to zero, effectively blocking them from using any GPU resources.
D. The Kubernetes cluster has no available GPUs.

Answer: C

NEW QUESTION # 63
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?

A. A Retrieval-Augmented Generation (RAG) architecture.
B. A new LLM trained from scratch on the legal documents.
C. A fine-tuning pipeline to update the LLM daily.
D. A predictive AI model to classify legal documents.

Answer: A

NEW QUESTION # 64
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