Network Appliance NetApp Certified AI Expert : NS0-901 exam

Network Appliance NS0-901 Actual PDF
  • Exam Code: NS0-901
  • Exam Name: NetApp Certified AI Expert Exam
  • Updated: Aug 06, 2026
  • Q & A: 106 Questions and Answers
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Network Appliance NS0-901 Exam Syllabus Topics:

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

Network Appliance NetApp Certified AI Expert Sample Questions:

1. The firm has acquired a competitor, and the volume of proprietary documents for the "Advisor Assistant" is expected to triple, exceeding the capacity of the current StorageGRID data lake. The architect needs to expand the data lake's capacity non-disruptively.
The current StorageGRID status is:
System_Health: Nominal
Node_Count: 6 (3 Storage Nodes, 3 Admin/Gateway Nodes)
Usable_Capacity: 1.5 PB
Used_Capacity: 1.4 PB (93%)
What is the standard procedure for scaling the capacity of the on-premises NetApp StorageGRID system?

A) Provision a new, separate StorageGRID system and use BlueXP copy and sync to move the data.
B) Add new storage nodes to the existing StorageGRID system and execute an expansion procedure.
C) Use FabricPool to tier the excess data from StorageGRID to a public cloud provider.
D) Replace the disks in the existing storage nodes with higher-capacity drives.


2. An architect is designing a comprehensive AI platform for a large enterprise. The platform must support the entire data lifecycle, from ingest at the edge to a central data lake, and finally to a high- performance training cluster.
The requirements are:
- Edge Ingest: Data must be collected at remote sites and efficiently replicated to the core.
- Data Lake: A central, petabyte-scale repository for unstructured data, accessible via the S3 protocol.
- Training Cluster: A high-performance compute cluster that requires low-latency, parallel file access to training datasets.
- Data Traceability: All datasets used for training must be immutably versioned.
Which combination of NetApp technologies and protocols should the architect choose to build this solution? (Select all that apply.)

A) Use NetApp StorageGRID to create the petabyte-scale, S3-accessible data lake at the core.
B) Use NetApp FlexCache to tier cold data from the data lake to the public cloud.
C) Use a NetApp E-Series system with a parallel file system (like BeeGFS) to provide high- performance, parallel file access for the training cluster.
D) Use NetApp Snapshots on the training dataset volumes to create immutable, point-in-time versions for traceability.
E) Use iSCSI as the primary protocol for the data lake to ensure maximum compatibility.
F) Use NetApp ONTAP systems at the edge and NetApp SnapMirror to replicate data to the core data center.


3. An AI architect is designing a storage solution for a new training cluster. The primary workload consists of training large language models, which involves sequential reads of massive datasets.
The key requirement is to maximize GPU utilization by providing the highest possible data throughput. Cost is a secondary concern to performance.
Which NetApp storage system is the most appropriate choice for this workload?

A) NetApp E-Series
B) NetApp C-Series
C) NetApp StorageGRID
D) NetApp ASA (All-SAN Array)


4. 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.


5. The company's finance department mandates a cost-control strategy for the petabyte-scale StorageGRID data lake. The analysis shows that 80% of the simulation data is not accessed after
90 days but must be retained for five years for regulatory compliance. Which StorageGRID feature should the architect use to automatically reduce the long-term storage costs for this inactive data?

A) Use NetApp SnapMirror to replicate the cold data to a different, lower-cost StorageGRID cluster.
B) Implement a FabricPool policy to tier the data to a NetApp ASA system.
C) Enable deduplication and compression on all StorageGRID storage nodes.
D) Configure an Information Lifecycle Management (ILM) policy that automatically moves objects older than 90 days to a lower-cost cloud archive tier, such as Amazon S3 Glacier Deep Archive.


Solutions:

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

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