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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q35-Q40):

NEW QUESTION # 35
A national research laboratory is investing in a turnkey AI infrastructure solution. Their primary goal is to eliminate the complexity and risk of designing and integrating the compute, network, and storage components themselves. The solution must be pre-validated by the vendors to deliver predictable, linear performance as they scale from one to multiple compute nodes. Which two options represent this type of pre-validated, converged infrastructure solution for AI? (Choose two)

A. A Cisco and NetApp FlexPod for AI solution.
B. A custom-built server rack with components sourced from various different vendors.
C. NetApp AIPod, which integrates NVIDIA DGX servers with NetApp storage and networking.
D. A public cloud provider's general-purpose virtual machine instances.
E. A single NetApp FAS system connected directly to a single server.

Answer: A,C

NEW QUESTION # 36
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 model was trained using supervised learning, which is inefficient for fraud detection.
B. The storage tier is too slow, causing the GPUs to wait for data.
C. The network latency is too high for an edge deployment.
D. The compute and storage infrastructure is sized for a large-scale training workload, not a lightweight inference workload.

Answer: D

NEW QUESTION # 37
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 Run:AI scheduler is offline and cannot process new jobs.
B. The job is requesting a specific type of GPU that is not available in the cluster.
C. The Kubernetes cluster has no available GPUs.
D. 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.

Answer: D

NEW QUESTION # 38
An architect is designing a fully automated, end-to-end MLOps pipeline on Kubernetes for a computer vision use case. The pipeline must handle everything from data versioning to model deployment.
The required pipeline stages are:
1. Data Versioning: Create a new, immutable version of the master dataset for the pipeline run.
2. Data Preparation: Launch a pod to run a preprocessing script on the versioned data.
3. Model Training: Launch a distributed training job that reads the prepared data from a highperformance volume.
4. Model Deployment: Push the trained model to a production inference service.
Which combination of NetApp and Kubernetes technologies provides the most effective and automated solution for this entire pipeline?

A. Use NetApp XCP to copy the data for each stage and configure static PersistentVolumes for each pod.
B. Manually create a NetApp Snapshot via System Manager before each pipeline run, and use the NetApp DataOps Toolkit only for the training stage.
C. Use a single, large ReadWriteMany PVC for all stages to simplify the pipeline configuration.
D. Use the NetApp DataOps Toolkit to create a Snapshot of the source data volume (for versioning), then create a FlexClone PVC from the snapshot for the preparation stage, and finally create a FlexGroup PVC for the training stage.
E. Use NetApp SnapMirror for data versioning and manually create hostPath volumes for each pipeline stage.

Answer: D

NEW QUESTION # 39
An organization wants to provide its data science team with a secure, on-demand method for using a powerful generative AI model with their private, sensitive corporate data. The solution must ensure that the private data is never exposed to the public internet or the public LLM API endpoint.
The architect is designing a solution using BlueXP.
Which two components are essential for building this secure solution? (Choose 2.)

A. The BlueXP GenAI Toolkit, which acts as a proxy to intercept user prompts and enrich them with data from a local vector database.
B. A policy in BlueXP classification to copy all sensitive data to a public cloud bucket for easier access.
C. A direct VPN connection from each data scientist's laptop to the public LLM provider.
D. A private vector database hosted on an on-premises NetApp ONTAP system to store embeddings of the sensitive corporate data.
E. A BlueXP Connector deployed in a public subnet with a public IP address to allow access to the LLM.

Answer: A,D

NEW QUESTION # 40
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