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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
A company is running its AI training workloads on a NetApp AFF A-Series system. To manage costs, they want to automatically move inactive training datasets and older model checkpoints from the high- performance all-flash tier to a lower-cost object storage tier, such as an on- premises StorageGRID or a public cloud bucket. The process must be transparent to the data scientists and not require changes to their scripts or file paths.
Which two NetApp technologies should be combined to achieve this goal? (Choose 2.)

Answer: B,C


NEW QUESTION # 18
An architect is designing a scalable, automated MLOps platform using Kubeflow on a Kubernetes cluster. The platform must support the entire AI lifecycle for multiple teams, with different storage requirements at each stage.
The key requirements are:
- Data Ingestion: A pipeline step needs a shared, read-write volume accessible by multiple pods to stage raw data.
- Experimentation: Data scientists need individual, isolated volumes for their Jupyter notebooks.
- Training: Distributed training jobs require a high-performance, parallel-access filesystem for reading training data.
- Automation: All storage must be provisioned automatically via Kubeflow pipeline definitions without manual intervention.
Which combination of technologies and configurations would create the most effective solution?

Answer: A,C


NEW QUESTION # 19
An AI infrastructure engineer is troubleshooting a poorly performing distributed training job. The job is running across multiple nodes, each equipped with powerful GPUs. The engineer observes that overall GPU utilization is unexpectedly low. System-level monitoring on the compute nodes provides the following metrics during a training run.
avg_gpu_utilization: 25%
avg_cpu_iowait_percent: 65%
avg_network_bandwidth_util: 95% (on a 10GbE network)
storage_array_latency: <1ms
Given these metrics, what is the most likely bottleneck causing the low GPU utilization?

Answer: B


NEW QUESTION # 20
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: B


NEW QUESTION # 21
Which storage protocols are commonly used for handling large-scale AI data? (Choose two)

Answer: A,B


NEW QUESTION # 22
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