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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. Optimizing data access and movement
  • 3. Maximizing performance in demanding AI workloads
Topic 2: AI Hardware Architectures18%- NetApp Architectures
  • 1. BasePod
  • 2. SuperPOD
  • 3. OVX architectures
- 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 Overview15%- AI Industry Applications
  • 1. Agents
  • 2. Digital twins
  • 3. Healthcare applications
- Training vs. Inferencing vs. Predictions
  • 1. Distinguish between training and inference workloads
- Algorithm Types
  • 1. Unsupervised learning
  • 2. Reinforcement learning
  • 3. Supervised learning
- Machine Learning Fundamentals
  • 1. Understand the relationship between AI, machine learning, and deep learning
  • 2. Describe machine learning benefits
- AI Convergence with HPC and Analytics
  • 1. Leveraging shared infrastructure for AI, HPC, and analytics
- AI Deployment Models
  • 1. On-premises
  • 2. Benefits and risks of each model
  • 3. Edge
  • 4. Cloud
Topic 4: AI Lifecycle27%- Predictive AI vs. Generative AI
  • 1. Distinction between predictive and generative AI
  • 2. Impact of generative content (text, images, video, decision-making)
  • 3. Large Language Models (LLMs)
- Model Development
  • 1. Fine-tuning workflows
  • 2. Model building
  • 3. Inferencing
- Data Preparation
  • 1. XCP and CopySync
  • 2. NetApp BlueXP Classification
  • 3. Data aggregation and cleansing
- Generative AI Concepts
  • 1. Retrieval Augmented Generation (RAG)
  • 2. Hallucinations
  • 3. Fine-tuning
Topic 5: 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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q66-Q71):

NEW QUESTION # 66
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: B,E


NEW QUESTION # 67
A security administrator is reviewing the configuration of a production ONTAP cluster after a compliance audit. The audit requires that all volumes containing financial data must be protected against ransomware. The administrator runs a command to check the status of Autonomous Ransomware Protection (ARP) on a critical volume.
The command and its output are as follows:
cluster-1::> security ransomware anti-ransomware show -vserver svm_finance -volume finance_q1_data Vserver: svm_finance Volume: finance_q1_data Autonomous Ransomware Protection Status: disabled Learning Mode: normal Dry Run Mode: false Action on Detection: notify Based on this output, what is the primary security risk for the 'finance_q1_data' volume?

Answer: A


NEW QUESTION # 68
An AI architect needs to design a complete, end-to-end data pipeline for a new generative AI application at a financial services firm. The application will allow internal analysts to query a massive, 500 TB archive of historical market data and reports to generate summaries. The firm has the following environment and requirements:
Data_Sources: A mix of on-premises ONTAP filers and StorageGRID S3 buckets.
Requirement_1: All queries must be answered using only the private data archive.
Requirement_2: All generated summaries must provide citations to the source reports.
Requirement_3: All data containing client PII must be identified and excluded from the LLM context.
Requirement_4: The solution must be cost-effective for the large, mostly-read data archive.
Which set of actions and technologies constitutes the most robust and compliant solution?
(Select all that apply.)

Answer: A,D,E,F


NEW QUESTION # 69
A financial services company is required by regulators to be able to trace any version of their deployed fraud detection model back to the exact dataset and source code commit used to train it.
The current MLOps workflow is as follows:
Code_Repository: Git (commit hash: a1b2c3d4)
Dataset_Location: /vol/prod_data/fraud_dataset_v3
Storage_System: NetApp ONTAP 9
Model_Output: /vol/models/fraud_model_v3.2
Which NetApp technology should be used to create an immutable, point-in-time, and space- efficient copy of the dataset that can be linked to the specific code commit and model version?

Answer: B


NEW QUESTION # 70
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: A


NEW QUESTION # 71
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