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Network Appliance NS0-901 Exam Syllabus Topics:

SectionWeightObjectives
AI Overview15%- AI Industry Applications
  • 1. Healthcare applications
  • 2. Agents
  • 3. Digital twins
- Machine Learning Fundamentals
  • 1. Describe machine learning benefits
  • 2. Understand the relationship between AI, machine learning, and deep learning
- Algorithm Types
  • 1. Reinforcement learning
  • 2. Unsupervised learning
  • 3. Supervised learning
- AI Deployment Models
  • 1. Edge
  • 2. On-premises
  • 3. Benefits and risks of each model
  • 4. Cloud
- 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
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
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. Scaling AI workloads with Kubernetes
  • 2. Leveraging BlueXP software tools
AI Lifecycle27%- Data Preparation
  • 1. XCP and CopySync
  • 2. Data aggregation and cleansing
  • 3. NetApp BlueXP Classification
- 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. Model building
  • 2. Inferencing
  • 3. Fine-tuning workflows
- Generative AI Concepts
  • 1. Hallucinations
  • 2. Fine-tuning
  • 3. Retrieval Augmented Generation (RAG)
AI Hardware Architectures18%- Networking and Storage
  • 1. Network protocols for AI workloads
  • 2. Storage architectures for AI
- NetApp Architectures
  • 1. OVX architectures
  • 2. BasePod
  • 3. SuperPOD
- Infrastructure Topologies
  • 1. Data aggregation and compute topologies

Network Appliance NetApp Certified AI Expert Sample Questions:

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 E-Series for the data lake and a NetApp ASA system for the vector database.
  • C. Use NetApp Cloud Volumes ONTAP for the data lake and NetApp StorageGRID for the vector database.
  • D. Use NetApp StorageGRID for the data lake and a NetApp ASA system for the vector database.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

A data scientist needs to test a new data normalization technique. To do this, they require an isolated, writable copy of a 50 TB curated simulation dataset that resides on the NetApp ASA system. The operation must be completed as quickly as possible and consume minimal additional storage space. Which NetApp technology is the most appropriate solution for this requirement?

  • A. NetApp SnapMirror
  • B. NetApp FlexClone
  • C. NetApp XCP
  • D. NetApp FabricPool
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

To meet HIPAA compliance, the first step in the data pipeline is to identify all medical scans that contain embedded PII. The solution must be automated and capable of scanning data in-place on the on-premises ASA system.
Which two technologies should be used to accomplish this identification and tagging task?
(Choose 2.)

  • A. NetApp SnapLock to make the source data immutable before scanning.
  • B. NetApp BlueXP classification, configured to scan the on-premises ASA working environment.
  • C. A custom "PII" category within BlueXP classification to identify specific medical record number formats.
  • D. A manual review process where technicians visually inspect each scan for PII.
  • E. A custom Python script that uses regular expressions to search file contents.
Reveal Solution  Discussion  0

Correct Answer: B,C  🗳️

Given the firm's requirements for using a private, constantly updated knowledge base and the strict mandate for data traceability, which AI architecture is the most appropriate foundation for the "Advisor Assistant" chatbot?

  • A. A standalone LLM deployed in an air-gapped environment with no access to the document repository.
  • B. A predictive AI model trained to forecast market trends.
  • C. A fine-tuning architecture where a base LLM is continuously retrained on the entire document repository.
  • D. A Retrieval-Augmented Generation (RAG) architecture that retrieves relevant context from a local vector database to enrich prompts sent to the LLM.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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

  • A. Predicting patient outcomes
  • B. Automating financial reporting
  • C. Diagnosing diseases
  • D. Managing hospital supply chains
Reveal Solution  Discussion  0

Correct Answer: A,C  🗳️

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