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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q95-Q100):

NEW QUESTION # 95
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?

Answer: A


NEW QUESTION # 96
An AI team is planning two separate projects. The architect needs to provision the appropriate infrastructure for each.
| | Project A | Project B|
| -- | | - |
| Goal | Build a novel image recognition model from scratch.
| Adapt an existing, pre- trained LLM to understand company-specific jargon. |
| Input Data | 10 million new, unlabeled images. | A 50 GB text corpus of internal documents. |
| Required Compute | Very High (Weeks of multi-GPU training) | Moderate (Hours of single-GPU training) | Which two statements accurately describe the infrastructure requirements for these projects?
(Choose two.)

Answer: A,E


NEW QUESTION # 97
Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)

Answer: B,D


NEW QUESTION # 98
A junior administrator is attempting to delete an old, unused Snapshot copy from a production volume to reclaim space but receives an error. The administrator is certain they are using the correct command and have the appropriate 'vsadmin' role.
The command and error are shown below:
cluster-1::> volume snapshot delete -vserver svm_prod -volume app_data -snapshot nightly.2025- 06-15_0015 Error: command failed: This operation is not permitted. Deletion of Snapshot copy "nightly.2025-
06-15_0015" on volume "app_data" in Vserver "svm_prod" requires approval.
Use the "security multi-admin-verify approval show" command to view pending approvals.
What is the most likely reason the administrator cannot delete the Snapshot?

Answer: C


NEW QUESTION # 99
The "Advisor Assistant" application, running as a pod in Kubernetes, suddenly cannot access its data on the AFF A-Series. The application logs show "connection timed out" errors. The network team provides a firewall log snippet for the traffic between the application pod and the storage system's NFS data LIF.
TIME | SRC_IP | DST_IP | PROTO | DST_PORT | ACTION
-|--||-|-|-
2025-07-11T16:01:10Z | 10.20.5.101 (Pod) | 10.20.10.55 (LIF) | TCP | 111 | BLOCKED 2025-07-
11T16:01:12Z | 10.20.5.101 (Pod) | 10.20.10.55 (LIF) | TCP | 2049 | BLOCKED What is the most likely cause of the connectivity failure?

Answer: B


NEW QUESTION # 100
......

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