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Network Appliance NS0-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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
Topic 2: AI Hardware Architectures18%- Networking and Storage
  • 1. Storage architectures for AI
  • 2. Network protocols for AI workloads
- Infrastructure Topologies
  • 1. Data aggregation and compute topologies
- NetApp Architectures
  • 1. BasePod
  • 2. SuperPOD
  • 3. OVX architectures
Topic 3: AI Lifecycle27%- Generative AI Concepts
  • 1. Hallucinations
  • 2. Retrieval Augmented Generation (RAG)
  • 3. Fine-tuning
- Model Development
  • 1. Model building
  • 2. Fine-tuning workflows
  • 3. Inferencing
- 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
Topic 4: AI Software Architectures18%- 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
- Development Tools
  • 1. NetApp DataOps Toolkit
  • 2. Jupyter notebooks vs. pipelines
Topic 5: AI Overview15%- AI Deployment Models
  • 1. Edge
  • 2. Benefits and risks of each model
  • 3. On-premises
  • 4. Cloud
- Algorithm Types
  • 1. Supervised learning
  • 2. Unsupervised learning
  • 3. Reinforcement learning
- AI Convergence with HPC and Analytics
  • 1. Leveraging shared infrastructure for AI, HPC, and analytics
- Machine Learning Fundamentals
  • 1. Describe machine learning benefits
  • 2. Understand the relationship between AI, machine learning, and deep learning
- Training vs. Inferencing vs. Predictions
  • 1. Distinguish between training and inference workloads
- AI Industry Applications
  • 1. Healthcare applications
  • 2. Agents
  • 3. Digital twins

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
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?

Answer: D


NEW QUESTION # 41
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?

Answer: D


NEW QUESTION # 42
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?

Answer: B


NEW QUESTION # 43
An architect is designing a fully automated, end-to-end MLOps pipeline on Kubernetes for a computer vision use case. The pipeline must handle everything from data versioning to model deployment.
The required pipeline stages are:
1. Data Versioning: Create a new, immutable version of the master dataset for the pipeline run.
2. Data Preparation: Launch a pod to run a preprocessing script on the versioned data.
3. Model Training: Launch a distributed training job that reads the prepared data from a highperformance volume.
4. Model Deployment: Push the trained model to a production inference service.
Which combination of NetApp and Kubernetes technologies provides the most effective and automated solution for this entire pipeline?

Answer: C


NEW QUESTION # 44
The data scientists report that their Kubernetes-based data preparation jobs are failing. The pods are stuck in a 'Pending' state.
An MLOps engineer runs 'kubectl describe pvc data-prep-pvc-01' and sees the following event:
Type Reason Age From Message
- - - -
Warning ProvisioningFailed 2m15s trident-orchestrator-7b... failed to provision volume with StorageClass "bronze-tier": backend unavailable: no healthy backend with satisfying attributes for storage class "bronze-tier" The engineer checks the Trident backend configurations and finds no backend associated with the "bronze-tier" StorageClass.
What is the root cause of the failure?

Answer: D


NEW QUESTION # 45
......

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