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Network Appliance NS0-901 Exam Syllabus Topics:
| Section | Weight | Objectives |
| AI Lifecycle | 27% | - Generative AI Concepts
- 1. Retrieval Augmented Generation (RAG)
- 2. Hallucinations
- 3. Fine-tuning
- 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. Model building
- 2. Inferencing
- 3. Fine-tuning workflows
|
| AI Software Architectures | 18% | - 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 Common Challenges | 22% | - 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
|
| AI Hardware Architectures | 18% | - NetApp Architectures
- 1. OVX architectures
- 2. SuperPOD
- 3. BasePod
- Networking and Storage
- 1. Network protocols for AI workloads
- 2. Storage architectures for AI
- Infrastructure Topologies
- 1. Data aggregation and compute topologies
|
| AI Overview | 15% | - AI Convergence with HPC and Analytics
- 1. Leveraging shared infrastructure for AI, HPC, and analytics
- Machine Learning Fundamentals
- 1. Understand the relationship between AI, machine learning, and deep learning
- 2. Describe machine learning benefits
- Algorithm Types
- 1. Supervised learning
- 2. Reinforcement learning
- 3. Unsupervised learning
- Training vs. Inferencing vs. Predictions
- 1. Distinguish between training and inference workloads
- AI Deployment Models
- 1. Benefits and risks of each model
- 2. Cloud
- 3. Edge
- 4. On-premises
- AI Industry Applications
- 1. Healthcare applications
- 2. Digital twins
- 3. Agents
|
Network Appliance NetApp Certified AI Expert Sample Questions:
Question 1
The architect needs to design an efficient data flow to move curated training sets from the central StorageGRID data lake to the high-performance NetApp ASA system used by the AI cluster. The process must be manageable from a single interface and should be automatable. Which two NetApp technologies should be used to implement this data pipeline stage? (Choose 2.)
A. A manual copy process using an S3 client on a bastion host.
B. NetApp FabricPool to automatically tier the data from StorageGRID to the ASA.
C. NetApp BlueXP copy and sync service to create and manage the data synchronization relationship between the S3 source and the NFS/CIFS destination.
D. A custom Python script using the NetApp DataOps Toolkit to trigger the data movement.
E. NetApp SnapMirror to replicate data from the StorageGRID object store to the ASA's file system.
Question 2
An organization is developing a new AI-powered application. The initial phase involves feeding a curated 50 TB dataset of labeled images into a complex neural network, allowing the model to learn and adjust its internal parameters over millions of iterations. The second phase involves deploying this finalized model to a web service where it will process single, user-uploaded images and return a classification in real-time.
Which statement accurately describes these two phases?
A. Phase 1 is inferencing, and Phase 2 is training.
B. Phase 1 is training, and Phase 2 is inferencing.
C. Both Phase 1 and Phase 2 are examples of training.
D. Both Phase 1 and Phase 2 are examples of inferencing.
Question 3
A junior administrator is attempting to delete an old, unused Snapshot copy from a production volume to reclaim space but receives an error. The administrator is certain they are using the correct command and have the appropriate 'vsadmin' role.
The command and error are shown below:
cluster-1::> volume snapshot delete -vserver svm_prod -volume app_data -snapshot nightly.2025- 06-15_0015 Error: command failed: This operation is not permitted. Deletion of Snapshot copy "nightly.2025-
06-15_0015" on volume "app_data" in Vserver "svm_prod" requires approval.
Use the "security multi-admin-verify approval show" command to view pending approvals.
What is the most likely reason the administrator cannot delete the Snapshot?
A. The volume is protected by SnapLock, and the Snapshot has not reached its expiry date.
B. The 'vsadmin' role does not have permission to delete Snapshots.
C. Multi-Admin Verification (MAV) is enabled, and the destructive operation requires approval from another designated administrator.
D. The Snapshot is locked by a SnapMirror relationship.
Question 4
An AI platform administrator is trying to deploy a new GenAI toolkit using the BlueXP Workload Factory. The deployment fails, and the administrator examines the API response from BlueXP.
{
"jobId": "we-deploy-genai-987zy",
"status": "FAILED",
"statusCode": 403,
"message":
"Forbidden: The service principal or user account used by the Connector does not have the required permissions on the target subscription to create a new resource group.
Required permission: 'Microsoft.Resources/subscriptions/resourcegroups/write'." } Based on this API response, what is the root cause of the deployment failure?
A. The GenAI toolkit requires a specific license that has not been added to the BlueXP digital wallet.
B. The Azure service principal associated with the BlueXP Connector lacks the necessary IAM role to create resource groups.
C. The BlueXP Connector is offline and cannot communicate with the cloud provider.
D. The selected ONTAP version does not support the GenAI toolkit.
Question 5
A university is building a shared AI research platform. They have two primary requirements:
1. Performance: A "hot" research area for active model training and development that requires the absolute lowest latency and highest throughput to support multiple, simultaneous GPU- intensive jobs.
The data in this area is around 50 TB.
2. Capacity & Cost: A "cold" data lake to store over 5 PB of raw, unstructured experimental data that is infrequently accessed but must be retained for compliance and future use. This tier must be as costeffective as possible.
Which combination of NetApp hardware and technologies should an architect select to build a complete, optimized, and cost-effective solution? (Select all that apply.)
A. Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
B. Use a standard 10GbE network for all connectivity to reduce costs.
C. Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
D. Use NetApp StorageGRID to build the 5 PB cost-effective data lake.
E. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.
F. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.
Solutions:
Question 1 Answer: C,D | Question 2 Answer: B | Question 3 Answer: C | Question 4 Answer: B | Question 5 Answer: A,C,D,E |