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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q103-Q108):

NEW QUESTION # 103
An organization has a core data center with a large AI training cluster and several remote edge locations for data ingest and local inference. The edge locations frequently need access to the latest models trained in the core data center, but WAN bandwidth is limited and can be unreliable.
Users at the edge are reporting slow model loading times.
An architect reviews the data access logs from an edge site:
Timestamp: 2025-07-11T15:30:00Z
Event: Model_Load_Request
Model_Path: nfs://core-filer.example.com/vol/models/latest_model.pkl
Source_IP: 192.168.100.15 (Edge Server)
Destination_IP: 10.1.1.50 (Core Filer)
Status: SUCCESS
Duration: 3600s (60 minutes)
What is the most likely cause of the slow model loading times at the edge?

A. The model file is being transferred over a slow, high-latency WAN link for every load request.
B. The edge server does not have enough RAM to cache the model effectively.
C. The core ONTAP filer is using slow, capacity-based disks.
D. The NFS version used between the core and edge is outdated.

Answer: A

NEW QUESTION # 104
A financial services company has deployed a real-time fraud detection model at the edge. The model is designed for low-latency inference. However, monitoring reports indicate that the infrastructure costs are excessively high, and GPU utilization is consistently low. The architect reviews the deployment configuration.
Instance_Type: NVIDIA DGX A100 (8 GPUs)
Storage_Tier: High-Performance All-Flash (NetApp ASA)
Network: 100GbE RoCE
GPU_Utilization_Avg: 5%
Monthly_Cost: $15,000
Workload_Profile: Low-volume, sporadic, real-time predictions
What is the most likely cause of the high costs and low utilization?

A. The compute and storage infrastructure is sized for a large-scale training workload, not a lightweight inference workload.
B. The network latency is too high for an edge deployment.
C. The model was trained using supervised learning, which is inefficient for fraud detection.
D. The storage tier is too slow, causing the GPUs to wait for data.

Answer: A

NEW QUESTION # 105
Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)

A. RunAI
B. KubeFlow
C. Google VertexAI
D. TensorFlow Extended

Answer: A,B

NEW QUESTION # 106
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 training.
B. Phase 1 is inferencing, and Phase 2 is training.
C. Phase 1 is training, and Phase 2 is inferencing.
D. Both Phase 1 and Phase 2 are examples of inferencing.

Answer: C

NEW QUESTION # 107
An architect is designing a global infrastructure for a company that develops AI for autonomous vehicles.
The design must accommodate three distinct locations and functions:
1. Edge (Test Tracks): Fleets of test cars generate 100s of TBs of sensor data per day. This data must be ingested locally with high performance.
2. Core (Primary Data Center): The raw data from all edge sites must be aggregated here. This location houses the primary data lake and the main GPU cluster for large-scale model training.
3. Cloud (Public Cloud Provider): Data scientists want to use cloud-native tools for experimental data processing and model development. They also need a cost-effective location for long-term archiving of raw data.
Which combination of deployment locations and NetApp technologies creates the most logical and efficient end-to-end solution?

A. Use Cloud Volumes ONTAP at the edge, NetApp StorageGRID at the core, and on-premises ONTAP for cloud archive.
B. Use NetApp ONTAP systems at the edge and core, and Cloud Volumes ONTAP in the public cloud.
Use SnapMirror to replicate data from edge to core, and FabricPool to tier data from the core to the cloud.
C. Use NetApp E-Series at the edge, NetApp ASA at the core, and NetApp StorageGRID in the cloud.
Use SnapMirror to move data between all three tiers.
D. Deploy a single, global NetApp StorageGRID across all three locations to act as a unified data plane.

Answer: B

NEW QUESTION # 108
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