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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Hardware Architectures	18%	- NetApp Architectures 1. SuperPOD 2. OVX architectures 3. BasePod - Infrastructure Topologies 1. Data aggregation and compute topologies - Networking and Storage 1. Storage architectures for AI 2. Network protocols for AI workloads
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. Jupyter notebooks vs. pipelines 2. NetApp DataOps Toolkit
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
AI Overview	15%	- AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - AI Industry Applications 1. Agents 2. Digital twins 3. Healthcare applications - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - Algorithm Types 1. Supervised learning 2. Unsupervised learning 3. Reinforcement learning - Machine Learning Fundamentals 1. Describe machine learning benefits 2. Understand the relationship between AI, machine learning, and deep learning - AI Deployment Models 1. Edge 2. Cloud 3. Benefits and risks of each model 4. On-premises
AI Lifecycle	27%	- Model Development 1. Fine-tuning workflows 2. Inferencing 3. Model building - Data Preparation 1. XCP and CopySync 2. Data aggregation and cleansing 3. NetApp BlueXP Classification - Generative AI Concepts 1. Hallucinations 2. Retrieval Augmented Generation (RAG) 3. Fine-tuning - Predictive AI vs. Generative AI 1. Impact of generative content (text, images, video, decision-making) 2. Large Language Models (LLMs) 3. Distinction between predictive and generative AI

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

NEW QUESTION # 16
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. Manually deploy all new application replicas to a single, new, very large "super-node".
B. Replace all existing GPUs with the next-generation model to increase the performance of each node.
C. Vertically scale the existing nodes by adding more RAM and CPU cores to each one.
D. Horizontally scale the cluster by adding new GPU-equipped nodes.
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: D,E

NEW QUESTION # 17
Which of the following are typical requirements for inferencing in AI models? (Choose two)

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

Answer: B,C

NEW QUESTION # 18
The "Advisor Assistant" application, running as a pod in Kubernetes, suddenly cannot access its data on the AFF A-Series. The application logs show "connection timed out" errors. The network team provides a firewall log snippet for the traffic between the application pod and the storage system's NFS data LIF.
TIME | SRC_IP | DST_IP | PROTO | DST_PORT | ACTION
-|--||-|-|-
2025-07-11T16:01:10Z | 10.20.5.101 (Pod) | 10.20.10.55 (LIF) | TCP | 111 | BLOCKED 2025-07-
11T16:01:12Z | 10.20.5.101 (Pod) | 10.20.10.55 (LIF) | TCP | 2049 | BLOCKED What is the most likely cause of the connectivity failure?

A. The AFF A-Series does not support the NFS protocol.
B. The application pod has incorrect mount options configured.
C. The ONTAP system's data LIF is offline.
D. A network firewall between the pod's subnet and the storage subnet is blocking the required NFS ports.

Answer: D

NEW QUESTION # 19
Which of the following describes the impact of generative AI in content creation?

A. Generative AI analyzes data without generating content.
B. Generative AI can create new content such as text, images, and videos.
C. Generative AI predicts customer preferences without creating new content.
D. Generative AI only processes pre-existing content.

Answer: B

NEW QUESTION # 20
An MLOps engineer is deploying a training pod that requires a high-performance volume. After applying the pod and PVC manifests, the pod remains in a 'Pending' state. The engineer runs
'kubectl describe pod training-pod-7d8c' and sees the following event:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 2m15s
default-scheduler 0/4 nodes are available: 1 node(s) had volume node affinity conflict, 3 node(s) didn't find available persistent volume to bind.
The engineer then inspects the associated PVC and sees its status is also 'Pending'.
What is the most likely cause of this issue?

A. The ONTAP backend is out of available capacity to provision a new volume.
B. The Kubernetes scheduler is malfunctioning and cannot assign pods to nodes.
C. The Trident controller pod has crashed and needs to be restarted.
D. The 'storageClassName' specified in the PVC does not match any existing StorageClass managed by Trident.

Answer: D

NEW QUESTION # 21
......

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