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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. Jupyter notebooks vs. pipelines 2. NetApp DataOps Toolkit - Scaling and Orchestration 1. Leveraging BlueXP software tools 2. Scaling AI workloads with Kubernetes
AI Overview	15%	- 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 - AI Industry Applications 1. Healthcare applications 2. Agents 3. Digital twins - AI Deployment Models 1. On-premises 2. Benefits and risks of each model 3. Edge 4. Cloud - 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. Supervised learning 3. Unsupervised learning
AI Lifecycle	27%	- Model Development 1. Inferencing 2. Fine-tuning workflows 3. Model building - Generative AI Concepts 1. Retrieval Augmented Generation (RAG) 2. Hallucinations 3. Fine-tuning - Predictive AI vs. Generative AI 1. Large Language Models (LLMs) 2. Distinction between predictive and generative AI 3. Impact of generative content (text, images, video, decision-making) - Data Preparation 1. XCP and CopySync 2. NetApp BlueXP Classification 3. Data aggregation and cleansing
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. Sizing storage and compute resources effectively 2. Controlling costs and securing storage
AI Hardware Architectures	18%	- NetApp Architectures 1. OVX architectures 2. SuperPOD 3. BasePod - Networking and Storage 1. Storage architectures for AI 2. Network protocols for AI workloads - Infrastructure Topologies 1. Data aggregation and compute topologies

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q104-Q109):

NEW QUESTION # 104
The pod running the vector database on the Kubernetes cluster fails to start. An MLOps engineer runs 'kubectl describe pod vector-db-pod-0' and sees the following event message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 30s default-scheduler 0/8 nodes are available: 8 node(s) did not match pod anti-affinity rules.
The pod's manifest contains the following 'affinity' definition:
affinity:
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- labelSelector:
matchExpressions:
- key: app
operator: In
values:
- vector-db
topologyKey: "kubernetes.io/hostname"
What is the most likely reason the pod cannot be scheduled?

A. The PersistentVolumeClaim for the pod is in a 'Pending' state.
B. The pod's anti-affinity rule prevents it from being scheduled on any node that is already running another pod with the label 'app=vector-db'.
C. The 'topologyKey' is invalid; it should be 'failure-domain.beta.kubernetes.io/zone'.
D. The 'vector-db-pod-0' is requesting more GPU resources than are available on any node.

Answer: B

NEW QUESTION # 105
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 replacement of all physical testing with digital simulations.
B. The convergence of AI, High-Performance Computing (HPC), and analytics on a unified data infrastructure.
C. The separation of AI and HPC into dedicated, air-gapped environments.
D. The exclusive use of public cloud resources for all computational tasks.

Answer: B

NEW QUESTION # 106
A pharmaceutical company is creating a "digital twin" of its manufacturing process. This involves running complex simulations (an HPC workload) that generate massive datasets.
The company wants to use this data immediately for two other purposes:
1. Analytics: Business analysts need to run complex queries on the simulation output using tools like Spark.
2. AI Training: Data scientists need to use the same output as a training set for a predictive maintenance model.
The company wants to avoid creating separate data silos for each workload.
Which two NetApp technologies are best suited for building a unified data lake that can efficiently serve all three workloads (HPC, Analytics, AI)? (Choose 2.)

A. NetApp Autonomous Ransomware Protection to secure the data from modification.
B. NetApp StorageGRID to provide a scalable, S3-native object store that integrates directly with modern analytics platforms like Spark.
C. NetApp ONTAP with FlexGroup volumes to provide high-throughput, parallel NFS access for the HPC and AI training workloads.
D. NetApp SnapCenter to create application-consistent backups of the data.
E. NetApp Keystone to provide a flexible, pay-as-you-go consumption model for the infrastructure.

Answer: B,C

NEW QUESTION # 107
Which of the following techniques are used to maximize GPU utilization in AI workloads? (Choose two)

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

Answer: A,B

NEW QUESTION # 108
A research institute is designing an infrastructure to support its entire AI drug discovery pipeline.
The pipeline has two distinct workload requirements:
1. Training: A team of data scientists needs to train several large transformer models simultaneously using a 500 TB dataset of genomic sequences. This process requires maximum data throughput to keep the GPUs saturated.
2. Inference: Once trained, the models are deployed to an internal web portal where researchers submit individual protein sequences for analysis. These queries must return results with the lowest possible latency.
Which infrastructure design best satisfies both requirements? (Choose 2.)

A. Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.
B. Use a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.
C. Configure QoS minimums on the training volumes to ensure they do not impact inference performance.
D. Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.
E. Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.

Answer: A,E

NEW QUESTION # 109
......

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