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Network Appliance NS0-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Common Challenges22%- Traceability and Optimization
  • 1. Ensuring traceability for code, data, and models
  • 2. Optimizing data access and movement
  • 3. Maximizing performance in demanding AI workloads
- Resource Management
  • 1. Sizing storage and compute resources effectively
  • 2. Controlling costs and securing storage
Topic 2: AI Overview15%- AI Convergence with HPC and Analytics
  • 1. Leveraging shared infrastructure for AI, HPC, and analytics
- AI Deployment Models
  • 1. Cloud
  • 2. Edge
  • 3. Benefits and risks of each model
  • 4. On-premises
- AI Industry Applications
  • 1. Digital twins
  • 2. Agents
  • 3. Healthcare applications
- 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
- Algorithm Types
  • 1. Reinforcement learning
  • 2. Supervised learning
  • 3. Unsupervised learning
Topic 3: AI Hardware Architectures18%- NetApp Architectures
  • 1. SuperPOD
  • 2. BasePod
  • 3. OVX architectures
- Infrastructure Topologies
  • 1. Data aggregation and compute topologies
- Networking and Storage
  • 1. Storage architectures for AI
  • 2. Network protocols for AI workloads
Topic 4: AI Lifecycle27%- Data Preparation
  • 1. NetApp BlueXP Classification
  • 2. XCP and CopySync
  • 3. Data aggregation and cleansing
- Generative AI Concepts
  • 1. Retrieval Augmented Generation (RAG)
  • 2. Hallucinations
  • 3. Fine-tuning
- Predictive AI vs. Generative AI
  • 1. Distinction between predictive and generative AI
  • 2. Large Language Models (LLMs)
  • 3. Impact of generative content (text, images, video, decision-making)
- Model Development
  • 1. Inferencing
  • 2. Model building
  • 3. Fine-tuning workflows
Topic 5: AI Software Architectures18%- Scaling and Orchestration
  • 1. Scaling AI workloads with Kubernetes
  • 2. Leveraging BlueXP software tools
- 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

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NS0-901 Downloadable PDF | New NS0-901 Study Materials

Because the NetApp Certified AI Expert Exam (NS0-901) test has a restricted time constraint, time management must be exercised to get success. Only with enough practice one can answer real Network Appliance NS0-901 Exam Questions in a given amount of time. It has created three formats to aid Network Appliance NS0-901 applicants in practicing and organizing their time for this aim.

Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
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: C


NEW QUESTION # 13
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: D


NEW QUESTION # 14
Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)

Answer: A,B


NEW QUESTION # 15
The firm's CFO is concerned about the rising costs of the on-premises AI infrastructure. A storage utilization report shows that of the 200 TB of data on the high-performance AFF A-Series, 150 TB consists of inactive, older versions of product documents that are rarely accessed but must be kept online for regulatory reasons.
The current storage landscape is:
- Performance Tier: NetApp AFF A-Series (200 TB used)
- Capacity Tier: NetApp StorageGRID (1.5 PB used)
What is the most effective and automated solution to reduce the storage cost of the performance tier without impacting data accessibility?

Answer: D


NEW QUESTION # 16
A financial services company has deployed a real-time fraud detection model at the edge. The model is designed for low-latency inference. However, monitoring reports indicate that the infrastructure costs are excessively high, and GPU utilization is consistently low. The architect reviews the deployment configuration.
Instance_Type: NVIDIA DGX A100 (8 GPUs)
Storage_Tier: High-Performance All-Flash (NetApp ASA)
Network: 100GbE RoCE
GPU_Utilization_Avg: 5%
Monthly_Cost: $15,000
Workload_Profile: Low-volume, sporadic, real-time predictions
What is the most likely cause of the high costs and low utilization?

Answer: B


NEW QUESTION # 17
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