Network Appliance NS0-901 : NetApp Certified AI Expert Exam

  • Exam Code: NS0-901
  • Exam Name: NetApp Certified AI Expert Exam
  • Updated: Aug 14, 2026
  • Q & A: 106 Questions and Answers

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Network Appliance NS0-901 Exam Syllabus Topics:

SectionWeightObjectives
AI Overview15%- 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
- Algorithm Types
  • 1. Unsupervised learning
  • 2. Supervised learning
  • 3. Reinforcement learning
- AI Convergence with HPC and Analytics
  • 1. Leveraging shared infrastructure for AI, HPC, and analytics
- AI Deployment Models
  • 1. Benefits and risks of each model
  • 2. On-premises
  • 3. Cloud
  • 4. Edge
- AI Industry Applications
  • 1. Agents
  • 2. Healthcare applications
  • 3. Digital twins
AI Hardware Architectures18%- Infrastructure Topologies
  • 1. Data aggregation and compute topologies
- NetApp Architectures
  • 1. SuperPOD
  • 2. BasePod
  • 3. OVX architectures
- Networking and Storage
  • 1. Storage architectures for AI
  • 2. Network protocols for AI workloads
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. Data aggregation and cleansing
  • 2. XCP and CopySync
  • 3. NetApp BlueXP Classification
- Model Development
  • 1. Inferencing
  • 2. Model building
  • 3. Fine-tuning workflows
- Generative AI Concepts
  • 1. Fine-tuning
  • 2. Hallucinations
  • 3. Retrieval Augmented Generation (RAG)
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. Maximizing performance in demanding AI workloads
  • 3. Ensuring traceability for code, data, and models
AI Software Architectures18%- 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
- Development Tools
  • 1. Jupyter notebooks vs. pipelines
  • 2. NetApp DataOps Toolkit

Network Appliance NetApp Certified AI Expert Sample Questions:

1. 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 Autonomous Ransomware Protection (ARP).
C) Use BlueXP backup and recovery to back up the volumes to the cloud more frequently.
D) Enable Multi-Admin Verification (MAV) on all volumes.


2. Which of the following applications use AI in the healthcare industry? (Choose two)

A) Predicting patient outcomes
B) Automating financial reporting
C) Diagnosing diseases
D) Managing hospital supply chains


3. The company decides to establish a disaster recovery (DR) site in a secondary data center for the entire Digital Twin platform. The DR plan must protect the HPC data, the AI training data, and the central data lake.
The DR requirements are:
- RPO: 4 hours for all data.
- RTO: 24 hours for the entire platform.
- Process: The failover and failback process should be as automated as possible.
Which combination of technologies provides the most comprehensive DR solution?

A) Use NetApp SnapMirror for the ASA, StorageGRID replication for the data lake, and rely on tape backups for the E-Series/BeeGFS data.
B) Use BlueXP backup and recovery to back up all three storage systems (ASA, E-Series, StorageGRID) to a single cloud target.
C) Deploy a MetroCluster configuration spanning the primary and DR sites for all three storage systems.
D) Use NetApp SnapMirror for the ASA, StorageGRID's built-in replication for the data lake, and a host- based tool like 'rsync' for the E-Series/BeeGFS data, all orchestrated via BlueXP disaster recovery.


4. Due to the success of the "Advisor Assistant," the number of concurrent users is expected to double in the next quarter. The existing Kubernetes cluster is running at 80% of its GPU capacity during peak hours. The architect must propose a plan to scale the compute infrastructure to handle the increased load.
Which two strategies represent the most effective and scalable solutions? (Choose 2.)

A) Configure the Kubernetes Cluster Autoscaler to automatically add new nodes to the cluster from a predefined node pool when GPU demand exceeds current capacity.
B) Horizontally scale the cluster by adding new GPU-equipped nodes.
C) Vertically scale the existing nodes by adding more RAM and CPU cores to each one.
D) Manually deploy all new application replicas to a single, new, very large "super-node".
E) Replace all existing GPUs with the next-generation model to increase the performance of each node.


5. An AI architect needs to design a complete, end-to-end data pipeline for a new generative AI application at a financial services firm. The application will allow internal analysts to query a massive, 500 TB archive of historical market data and reports to generate summaries. The firm has the following environment and requirements:
Data_Sources: A mix of on-premises ONTAP filers and StorageGRID S3 buckets.
Requirement_1: All queries must be answered using only the private data archive.
Requirement_2: All generated summaries must provide citations to the source reports.
Requirement_3: All data containing client PII must be identified and excluded from the LLM context.
Requirement_4: The solution must be cost-effective for the large, mostly-read data archive.
Which set of actions and technologies constitutes the most robust and compliant solution?
(Select all that apply.)

A) Use SnapMirror to replicate the StorageGRID data lake to a high-performance NetApp ASA system for faster query performance.
B) Fine-tune a foundation model on the entire 500 TB dataset to ensure it understands the financial context.
C) Implement a Retrieval-Augmented Generation (RAG) architecture to meet the requirements for private data usage and source citation.
D) During the RAG retrieval step, filter out any documents tagged as containing PII by BlueXP classification before sending them to the LLM.
E) Deploy BlueXP classification to scan the entire StorageGRID data lake to identify and tag all files containing PII.
F) Use NetApp XCP to perform a one-time migration of all data from the ONTAP filers to the StorageGRID data lake.


Solutions:

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

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