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Network Appliance NS0-901 : NetApp Certified AI Expert Exam

NS0-901

Exam Code: NS0-901

Exam Name: NetApp Certified AI Expert Exam

Updated: Oct 11, 2026

Q & A: 106 Questions and Answers

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Network Appliance NS0-901 Exam Syllabus Topics:

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

Network Appliance NetApp Certified AI Expert Sample Questions:

Question #1
An AI architect is designing a solution for a legal firm. The primary goal is to allow lawyers to ask natural language questions about case law stored in a private, 50 TB document repository.
The key project constraints are as follows:
Project_Goal: Answer questions using proprietary, real-time legal documents.
Constraint_1: Must not alter the foundational LLM's weights due to compliance.
Constraint_2: Case law database is updated daily with new rulings.
Constraint_3: All generated answers must be traceable to a source document.
Which technology should the architect choose as the core of this solution?

A. A predictive AI model to classify legal documents.
B. A fine-tuning pipeline to update the LLM daily.
C. A Retrieval-Augmented Generation (RAG) architecture.
D. A new LLM trained from scratch on the legal documents.


Question #2
What is the primary architectural benefit of using technologies like RDMA (Remote Direct Memory Access) and GPUDirect Storage in a high-performance AI training cluster?

A. They enable the use of lower-cost Ethernet switches in place of InfiniBand fabrics without any performance degradation.
B. They reduce the power consumption of the storage array by offloading checksum calculations.
C. They allow GPUs to communicate directly with each other and with NVMe storage, bypassing the host CPU and system memory, which significantly reduces I/O latency and CPU overhead.
D. They automatically encrypt data in-flight between the GPU servers and the storage system.


Question #3
An organization recently suffered a ransomware attack that encrypted several volumes on their primary ONTAP storage system, including a critical volume containing curated training data. The security team needs to implement a solution that can proactively detect and block ransomware- like file I/O patterns and automatically create a secure Snapshot copy before any damage is done.
The current ONTAP configuration is as follows:
ONTAP_Version: 9.12.1
Security_Features: SnapLock (Compliance Mode) on archive volumes
Anti-Virus_Scan: Enabled (Vscan)
Ransomware_Detection: Not configured
Which ONTAP feature should be enabled to provide this proactive, automated protection?

A. Increase the frequency of scheduled Snapshots.
B. Enable Multi-Admin Verification (MAV) on all volumes.
C. Use BlueXP backup and recovery to back up the volumes to the cloud more frequently.
D. Enable Autonomous Ransomware Protection (ARP).


Question #4
The pod running the vector database on the Kubernetes cluster fails to start. An MLOps engineer runs 'kubectl describe pod vector-db-pod-0' and sees the following event message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 30s default-scheduler 0/8 nodes are available: 8 node(s) did not match pod anti-affinity rules.
The pod's manifest contains the following 'affinity' definition:
affinity:
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- labelSelector:
matchExpressions:
- key: app
operator: In
values:
- vector-db
topologyKey: "kubernetes.io/hostname"
What is the most likely reason the pod cannot be scheduled?

A. The pod's anti-affinity rule prevents it from being scheduled on any node that is already running another pod with the label 'app=vector-db'.
B. The 'vector-db-pod-0' is requesting more GPU resources than are available on any node.
C. The 'topologyKey' is invalid; it should be 'failure-domain.beta.kubernetes.io/zone'.
D. The PersistentVolumeClaim for the pod is in a 'Pending' state.


Question #5
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.)

A. Use NetApp SnapMirror to create an asynchronous replication relationship for the AFF A-Series volume containing the vector database, with a schedule of 10 minutes.
B. Use BlueXP disaster recovery to orchestrate and automate the failover and failback workflows for the application and its data dependencies.
C. Rely on tape backups to be shipped to the DR site in the event of a disaster.
D. Use the NetApp DataOps Toolkit to manually script the failover process.
E. Use NetApp FlexCache at the DR site to cache data from the primary site.
F. Use StorageGRID's built-in replication rules to replicate object data from the primary site's grid to a StorageGRID instance at the DR site.


Solutions:

Question #1
Correct Answer: C
Question #2
Correct Answer: C
Question #3
Correct Answer: D
Question #4
Correct Answer: A
Question #5
Correct Answer: A,B,F

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