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Network Appliance NS0-901 Exam Syllabus Topics:

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

Network Appliance NetApp Certified AI Expert Sample Questions:

Question 1

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

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


Question 2

An AI platform team is investigating poor I/O performance for a specific workload that involves processing hundreds of thousands of small metadata files. The application is running on a Kubernetes cluster with storage provided by a NetApp ONTAP system over NFS. Performance metrics show acceptable network throughput but very high latency for metadata operations (e.g., open, stat, close).
The current storage configuration is as follows:
Storage_System: NetApp AFF A-Series
Protocol: NFSv4.1
Workload_Profile: Metadata-intensive, many small file lookups
Observed_Issue: High latency on metadata operations, slow job completion Which storage architecture would be better suited to handle this specific metadata-intensive workload?

A. A NetApp StorageGRID object storage system.
B. A NetApp E-Series system running a parallel file system like BeeGFS.
C. A NetApp Cloud Volumes ONTAP instance in a different region.
D. A NetApp SnapLock-enabled volume for data protection.


Question 3

The AI training jobs on the AIPod are performing below expectations. The NVIDIA DGX servers' GPUs show low utilization. A performance analysis reveals that the bottleneck is not the storage system itself, but the network path between the storage and the compute nodes.
The current network configuration is as follows:
Network_Fabric: 100GbE Standard Ethernet
Protocol: NFS over TCP/IP
Data_Path: Storage -> Host CPU -> GPU Memory
Which network architecture enhancement would provide the most significant performance improvement by reducing latency and CPU overhead?

A. Isolate the storage traffic on a separate VLAN from the management traffic.
B. Upgrade the network switches to a model with a larger packet buffer.
C. Add more 100GbE network ports to the storage controllers.
D. Implement RDMA over Converged Ethernet (RoCE) and configure GPUDirect Storage.


Question 4

The Chief Information Security Officer (CISO) is concerned about the risk of a ransomware attack encrypting the critical vector database hosted on the NetApp AFF A-Series. The CISO wants a solution that can proactively detect and block a live attack in real-time, not just recover from a backup after the fact.
Which NetApp security feature should the architect enable on the vector database volume to meet this requirement?

A. Multi-Admin Verification (MAV)
B. Autonomous Ransomware Protection (ARP)
C. NetApp SnapLock (Compliance Mode)
D. NetApp Volume Encryption (NVE)


Question 5

An organization is planning to deploy a large AI infrastructure but wants to avoid a large, upfront capital expenditure. They prefer an operational expenditure (OpEx) model where they pay for storage and compute resources as they are consumed. They also need the flexibility to scale resources up or down based on project demands.
Which NetApp consumption model is specifically designed to meet these financial and operational requirements?

A. NetApp Cloud Volumes ONTAP PAYGO licensing only.
B. NetApp Keystone, which provides a subscription-based, pay-as-you-go service for on-premises and cloud storage.
C. A standard capital purchase of AFF and ASA systems.
D. A perpetual licensing model for all ONTAP software features.


Solutions:

Question 1
Answer: B,D
Question 2
Answer: B
Question 3
Answer: D
Question 4
Answer: B
Question 5
Answer: B

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