Network Appliance NS0-901 - NetApp Certified AI Expert Exam

Network Appliance NS0-901 Actual PDF
  • Exam Code: NS0-901
  • Exam Name: NetApp Certified AI Expert Exam
  • Updated: Aug 16, 2026
  • Q & A: 106 Questions and Answers
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Network Appliance NS0-901 Exam Syllabus Topics:

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

Network Appliance NetApp Certified AI Expert Sample Questions:

1. A media company is building a new generative AI service. The project has two main components:
1. Data Lake & Fine-Tuning: A 300 TB repository of unstructured data (videos, images, text) stored as objects will be used to fine-tune a foundational model. This process requires a scalable, cost-effective storage solution that can integrate with cloud-native data processing tools like Apache Spark.
2. Inference & RAG: The fine-tuned model will be used in a customer-facing application that leverages Retrieval-Augmented Generation (RAG). To ensure low-latency responses, the RAG component requires extremely fast lookups from a 10 TB vector database.
The company needs a solution that optimizes both cost and performance for this entire lifecycle.
Which combination of NetApp technologies provides the most appropriate solution for this scenario?

A) Use a single, large NetApp ASA system for both the object data lake and the vector database.
B) Use NetApp Cloud Volumes ONTAP for the data lake and NetApp StorageGRID for the vector database.
C) Use NetApp E-Series for the data lake and a NetApp ASA system for the vector database.
D) Use NetApp StorageGRID for the data lake and a NetApp ASA system for the vector database.


2. An automotive company runs crash simulations on a dedicated High-Performance Computing (HPC) cluster and trains computer vision models on a separate AI cluster. Data scientists are complaining about the long delays required to move terabytes of simulation output data from the HPC storage to the AI cluster's storage before they can begin training.
The current data flow is as follows:
HPC Cluster -> --Manual Copy (NFS)--> -> AI Cluster
An architect has been asked to redesign the infrastructure to eliminate this data movement bottleneck.
Which architectural change would be most effective?

A) Install faster CPUs in the AI cluster's storage controllers.
B) Implement a converged data infrastructure where both the HPC and AI clusters access a single, high- performance data lake built on NetApp storage.
C) Upgrade the network connection between the two storage systems to 200GbE.
D) Use NetApp XCP to perform the data copy, as it is faster than a standard NFS copy.


3. The firm's data science team needs to run a high-priority, interactive model analysis job that requires immediate access to two GPUs. However, all GPUs in the cluster are currently allocated to long-running, lower-priority batch training jobs.
The MLOps platform, Run:AI, shows the following queue status:
JOB_ID | PROJECT | STATUS | PRIORITY | GPU_ALLOCATED
||--|-|
batch_job_1 | team_a | Running | Low | 2
batch_job_2 | team_a | Running | Low | 2
batch_job_3 | team_b | Running | Low | 4
interactive_1| team_c | Pending | High | 2 (requested)
How does the Run:AI platform address this resource contention to allow the high-priority job to run?

A) It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.
B) It automatically pauses one of the low-priority jobs, saves its state, and allocates its GPUs to the high- priority job, placing the paused job back in the queue.
C) It sends an email notification to the administrator to manually reallocate the GPUs.
D) It automatically terminates all low-priority jobs to free up the entire cluster.


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


5. What is the primary reason for implementing a hybrid cloud architecture in this medical imaging scenario?

A) To simplify network management by using a single, global virtual network spanning the on- premises and cloud environments.
B) To exclusively use cloud-native AI services, eliminating the need for any on-premises hardware.
C) To leverage the powerful, elastic GPU compute resources available in the public cloud for model training while keeping sensitive source data on-premises to meet security and HIPAA compliance requirements.
D) To reduce the overall cost of storage by keeping all data in the public cloud.


Solutions:

Question # 1
Answer: D
Question # 2
Answer: B
Question # 3
Answer: B
Question # 4
Answer: B
Question # 5
Answer: C

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