Network Appliance NS0-901 Questions - Latest NS0-901 Dumps [2026]

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Network Appliance NS0-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Software Architectures18%- Scaling and Orchestration
  • 1. Leveraging BlueXP software tools
  • 2. Scaling AI workloads with Kubernetes
- 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
Topic 2: AI Overview15%- Training vs. Inferencing vs. Predictions
  • 1. Distinguish between training and inference workloads
- Algorithm Types
  • 1. Supervised learning
  • 2. Reinforcement learning
  • 3. Unsupervised learning
- AI Convergence with HPC and Analytics
  • 1. Leveraging shared infrastructure for AI, HPC, and analytics
- AI Deployment Models
  • 1. On-premises
  • 2. Cloud
  • 3. Edge
  • 4. Benefits and risks of each model
- AI Industry Applications
  • 1. Agents
  • 2. Healthcare applications
  • 3. Digital twins
- Machine Learning Fundamentals
  • 1. Understand the relationship between AI, machine learning, and deep learning
  • 2. Describe machine learning benefits
Topic 3: AI Lifecycle27%- Data Preparation
  • 1. XCP and CopySync
  • 2. NetApp BlueXP Classification
  • 3. Data aggregation and cleansing
- Generative AI Concepts
  • 1. Retrieval Augmented Generation (RAG)
  • 2. Fine-tuning
  • 3. Hallucinations
- 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)
- Model Development
  • 1. Model building
  • 2. Fine-tuning workflows
  • 3. Inferencing
Topic 4: AI Hardware Architectures18%- Networking and Storage
  • 1. Network protocols for AI workloads
  • 2. Storage architectures for AI
- NetApp Architectures
  • 1. SuperPOD
  • 2. BasePod
  • 3. OVX architectures
- Infrastructure Topologies
  • 1. Data aggregation and compute topologies
Topic 5: AI Common Challenges22%- Resource Management
  • 1. Sizing storage and compute resources effectively
  • 2. Controlling costs and securing storage
- Traceability and Optimization
  • 1. Maximizing performance in demanding AI workloads
  • 2. Optimizing data access and movement
  • 3. Ensuring traceability for code, data, and models

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

NEW QUESTION # 35
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?

Answer: A,E


NEW QUESTION # 36
An internal audit requires the firm to prove the exact state of the financial product documentation that was used by the chatbot to answer a specific query from last Tuesday. The query log shows the request was processed at '2025-07-11T14:30:00Z'. The data pipeline that updates the vector database runs daily at midnight.
Which NetApp technology allows the team to instantly access an immutable, point-in-time version of the vector database volume as it existed on that specific day?

Answer: B


NEW QUESTION # 37
An organization is planning to deploy a large AI infrastructure but wants to avoid a large, upfront capital expenditure. They prefer an operational expenditure (OpEx) model where they pay for storage and compute resources as they are consumed. They also need the flexibility to scale resources up or down based on project demands.
Which NetApp consumption model is specifically designed to meet these financial and operational requirements?

Answer: D


NEW QUESTION # 38
An AI architect is planning the resource allocation for a new project. The primary task is to process millions of unlabeled customer reviews to identify naturally occurring groups or themes without any prior guidance.
The project requirements are summarized below:
Task: Discover hidden patterns in text data
Input_Data: 10 million unlabeled text reviews
Output: Clustered groups of related reviews
Supervision: None
Which type of machine learning algorithm is required for this task?

Answer: A


NEW QUESTION # 39
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?

Answer: A


NEW QUESTION # 40
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