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Network Appliance NS0-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Lifecycle27%- 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)
- Generative AI Concepts
  • 1. Retrieval Augmented Generation (RAG)
  • 2. Hallucinations
  • 3. Fine-tuning
- Model Development
  • 1. Inferencing
  • 2. Fine-tuning workflows
  • 3. Model building
- Data Preparation
  • 1. Data aggregation and cleansing
  • 2. XCP and CopySync
  • 3. NetApp BlueXP Classification
Topic 2: 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
Topic 3: AI Overview15%- AI Convergence with HPC and Analytics
  • 1. Leveraging shared infrastructure for AI, HPC, and analytics
- AI Industry Applications
  • 1. Digital twins
  • 2. Agents
  • 3. Healthcare applications
- 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
- Algorithm Types
  • 1. Reinforcement learning
  • 2. Supervised learning
  • 3. Unsupervised learning
- Training vs. Inferencing vs. Predictions
  • 1. Distinguish between training and inference workloads
Topic 4: AI Hardware Architectures18%- NetApp Architectures
  • 1. BasePod
  • 2. SuperPOD
  • 3. OVX architectures
- Networking and Storage
  • 1. Network protocols for AI workloads
  • 2. Storage architectures for AI
- Infrastructure Topologies
  • 1. Data aggregation and compute topologies
Topic 5: AI Common Challenges22%- Traceability and Optimization
  • 1. Maximizing performance in demanding AI workloads
  • 2. Optimizing data access and movement
  • 3. Ensuring traceability for code, data, and models
- Resource Management
  • 1. Controlling costs and securing storage
  • 2. Sizing storage and compute resources effectively

Network Appliance NetApp Certified AI Expert Sample Questions:

1. The firm decides to expand the "Advisor Assistant" project to a new team in a different department. This team needs its own isolated environment. An MLOps engineer attempts to submit a new GPU- intensive job for the new team, but it remains pending. The engineer checks the Run:AI scheduler logs and finds the following entry:
time="2025-07-11T16:45:00Z" level=info msg="Job ds-new-team-job1 cannot be scheduled.
Project 'new-team-project' has exceeded its GPU quota. Quota: 0, Requested: 1, Used: 0." What is the root cause of the scheduling failure?

A) The Run:AI scheduler is offline and cannot process new jobs.
B) A Run:AI project quota has been configured for the new team, but it has been set to zero, effectively blocking them from using any GPU resources.
C) The Kubernetes cluster has no available GPUs.
D) The job is requesting a specific type of GPU that is not available in the cluster.


2. Which of the following describes the impact of generative AI in content creation?

A) Generative AI analyzes data without generating content.
B) Generative AI only processes pre-existing content.
C) Generative AI predicts customer preferences without creating new content.
D) Generative AI can create new content such as text, images, and videos.


3. An AI team is embarking on a project to train a new, large-scale computer vision model from scratch. The lead architect emphasizes that the success of the project depends on four fundamental inputs that must be available and managed throughout the training process. Which of the following are the four essential requirements for model generation?

A) A project manager, a data scientist, a software engineer, and a budget.
B) Data, code, compute, and time.
C) A data lake, a data warehouse, a data pipeline, and a data mart.
D) A pre-trained model, a validation set, an inference engine, and a cloud provider.


4. The HPC cluster generates simulation data at an extremely high rate, requiring a storage system that can handle massively parallel writes from hundreds of compute nodes simultaneously. Which storage system and file protocol combination is the most appropriate choice for the HPC cluster's high-performance scratch space?

A) A NetApp StorageGRID system accessed via the S3 protocol.
B) A NetApp ASA system serving a single, large NFS volume.
C) A Cloud Volumes ONTAP instance with a standard file system.
D) A NetApp E-Series system serving a BeeGFS parallel file system.


5. A network administrator, attempting to harden the security of the data center, modifies a firewall access control list (ACL). Immediately afterward, the "Advisor Assistant" application pods can no longer mount their required NFS volumes from the AFF A-Series. The MLOps team confirms the pods are stuck in a 'ContainerCreating' state with a 'MountVolume.SetUp failed... connection timed out' error. The administrator provides the new, active firewall rule:
RULE | ACTION | PROTOCOL | SOURCE_IP_RANGE | DEST_IP_RANGE | DEST_PORT --|--|-
|--||--51 | ALLOW | TCP | 10.20.5.0/24 | 10.20.10.0/24 | 2049
What is the most likely reason for the mount failures?

A) The firewall rule is blocking the NFS lock manager (NLM) protocol, which is required for file locking.
B) The firewall rule is blocking the Portmapper/RPCbind service (TCP/UDP port 111), which is necessary for the initial NFS mount negotiation.
C) The 'SOURCE_IP_RANGE' is incorrect and does not include the Kubernetes pod IP addresses.
D) The firewall rule is blocking the NFSv4 protocol, which requires TCP port 2050.


Solutions:

Question # 1
Answer: B
Question # 2
Answer: D
Question # 3
Answer: B
Question # 4
Answer: D
Question # 5
Answer: B

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