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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q39-Q44):

NEW QUESTION # 39
A network administrator, attempting to harden the security of the data center, modifies a firewall access control list (ACL). Immediately afterward, the "Advisor Assistant" application pods can no longer mount their required NFS volumes from the AFF A-Series. The MLOps team confirms the pods are stuck in a 'ContainerCreating' state with a 'MountVolume.SetUp failed... connection timed out' error. The administrator provides the new, active firewall rule:
RULE | ACTION | PROTOCOL | SOURCE_IP_RANGE | DEST_IP_RANGE | DEST_PORT --|--|-
|--||--51 | ALLOW | TCP | 10.20.5.0/24 | 10.20.10.0/24 | 2049
What is the most likely reason for the mount failures?

Answer: D


NEW QUESTION # 40
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.)

Answer: A,C,D,F


NEW QUESTION # 41
A healthcare organization plans to use a large dataset of patient records to train a predictive model. Before training, they must identify and segregate all records containing Personally Identifiable Information (PII) to comply with privacy regulations. The data resides on an on- premises NetApp ONTAP cluster. The organization needs an automated tool to scan the data in- place and tag files containing PII without moving the data.
The project requirements are as follows:
Task: Identify PII in a large dataset.
Data_Location: On-premises ONTAP cluster.
Constraint: Data must not be moved from its source location for scanning.
Output: Tagged files containing PII.
Which NetApp tool is designed for this specific task?

Answer: A


NEW QUESTION # 42
An architect is designing an AI solution for a European hospital chain to analyze patient diagnostic scans. The project is subject to strict GDPR regulations, which mandate that patient data cannot leave the sovereign territory. The application also requires near-instantaneous results for physicians reviewing the scans in the hospital.
Which deployment model best satisfies these security and performance requirements?

Answer: C


NEW QUESTION # 43
A research institute is designing an infrastructure to support its entire AI drug discovery pipeline.
The pipeline has two distinct workload requirements:
1. Training: A team of data scientists needs to train several large transformer models simultaneously using a 500 TB dataset of genomic sequences. This process requires maximum data throughput to keep the GPUs saturated.
2. Inference: Once trained, the models are deployed to an internal web portal where researchers submit individual protein sequences for analysis. These queries must return results with the lowest possible latency.
Which infrastructure design best satisfies both requirements? (Choose 2.)

Answer: A,E


NEW QUESTION # 44
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