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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q93-Q98):

NEW QUESTION # 93
Which AI technology is used to generate new, never-before-seen content such as images or text?

Answer: C


NEW QUESTION # 94
An MLOps engineer is troubleshooting a failed Kubeflow pipeline step. The step was designed to create a clone of a dataset for a training job using the NetApp DataOps Toolkit. The pod logs for the failed pipeline step show the following:
Traceback (most recent call last):
File "create_clone.py", line 15, in <module>
clone_pvc(source_pvc_name="training-data-v2", new_pvc_name="train-job-34a-data") NameError: name 'clone_pvc' is not defined The engineer reviews the Python script for the pipeline step:
# create_clone.py
import os
from netapp_dataops.k8s import create_pvc
# Other code
print("Cloning source dataset for training run...")
clone_pvc(
source_pvc_name="training-data-v2",
new_pvc_name="train-job-34a-data"
)
print("Clone created successfully.")
What is the cause of the error?

Answer: D


NEW QUESTION # 95
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 # 96
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,B,E,F


NEW QUESTION # 97
Given the company's goal of combining physics-based simulations with AI-driven analytics on a shared data foundation, which industry trend does this project best represent?

Answer: B


NEW QUESTION # 98
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