Dumps NS0-901 Download | Exam NS0-901 Quick Prep

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Network Appliance NS0-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Overview15%- AI Deployment Models
  • 1. Edge
  • 2. On-premises
  • 3. Cloud
  • 4. Benefits and risks of each model
- 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
- Machine Learning Fundamentals
  • 1. Describe machine learning benefits
  • 2. Understand the relationship between AI, machine learning, and deep learning
- Algorithm Types
  • 1. Unsupervised learning
  • 2. Supervised learning
  • 3. Reinforcement learning
- AI Industry Applications
  • 1. Healthcare applications
  • 2. Digital twins
  • 3. Agents
Topic 2: AI Software Architectures18%- 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. Scaling AI workloads with Kubernetes
  • 2. Leveraging BlueXP software tools
Topic 3: AI Hardware Architectures18%- 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
Topic 4: AI Lifecycle27%- Generative AI Concepts
  • 1. Retrieval Augmented Generation (RAG)
  • 2. Fine-tuning
  • 3. Hallucinations
- Data Preparation
  • 1. XCP and CopySync
  • 2. Data aggregation and cleansing
  • 3. NetApp BlueXP Classification
- Predictive AI vs. Generative AI
  • 1. Distinction between predictive and generative AI
  • 2. Impact of generative content (text, images, video, decision-making)
  • 3. Large Language Models (LLMs)
- Model Development
  • 1. Inferencing
  • 2. Fine-tuning workflows
  • 3. Model building
Topic 5: AI Common Challenges22%- Traceability and Optimization
  • 1. Maximizing performance in demanding AI workloads
  • 2. Ensuring traceability for code, data, and models
  • 3. Optimizing data access and movement
- Resource Management
  • 1. Sizing storage and compute resources effectively
  • 2. Controlling costs and securing storage

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q28-Q33):

NEW QUESTION # 28
The firm decides to implement a disaster recovery (DR) site for the "Advisor Assistant" application in a secondary data center. The Recovery Point Objective (RPO) is 15 minutes, and the Recovery Time Objective (RTO) is 4 hours. The design must protect both the document data lake and the vector database.
The primary site contains:
- Data Lake: NetApp StorageGRID
- Vector DB: NetApp AFF A-Series
Which combination of technologies and processes provides a complete and robust DR solution?
(Select all that apply.)

Answer: C,D,E


NEW QUESTION # 29
A data science team works primarily at a central data center but needs to run a short-term, burst- compute training job in the public cloud to take advantage of specialized GPUs that are not available on- premises. They need to efficiently and securely move a 20 TB dataset from their on- premises ONTAP cluster to a Cloud Volumes ONTAP instance for the duration of the job.
The data flow requirement is as follows:
Source: On-premises ONTAP cluster
Destination: Cloud Volumes ONTAP in AWS
Requirement: Efficient, secure, block-level data transfer.
Which NetApp technology is the most appropriate tool for this task?

Answer: B


NEW QUESTION # 30
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: A


NEW QUESTION # 31
Which of the following are typical requirements for inferencing in AI models? (Choose two)

Answer: A,D


NEW QUESTION # 32
The data anonymization job, running in a Kubernetes pod, fails. The pod logs show a "Permission Denied" error when trying to access the source volume on the on-premises ASA. An administrator checks the export policy rule for the volume.
The active rule is as follows:
Rule_Index: 1
Client_Match: 10.50.0.0/16
Protocols: nfs4
Read_Only_Access: sys
Read_Write_Access: -
Superuser_Access: none
The Kubernetes pod that failed has an IP address of '10.60.5.10'.
What is the cause of the "Permission Denied" error?

Answer: C


NEW QUESTION # 33
......

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