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Network Appliance NS0-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Hardware Architectures18%- Infrastructure Topologies
  • 1. Data aggregation and compute topologies
- Networking and Storage
  • 1. Network protocols for AI workloads
  • 2. Storage architectures for AI
- NetApp Architectures
  • 1. BasePod
  • 2. OVX architectures
  • 3. SuperPOD
Topic 2: AI Lifecycle27%- Data Preparation
  • 1. NetApp BlueXP Classification
  • 2. Data aggregation and cleansing
  • 3. XCP and CopySync
- Model Development
  • 1. Model building
  • 2. Inferencing
  • 3. Fine-tuning workflows
- 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
Topic 3: AI Common Challenges22%- Resource Management
  • 1. Sizing storage and compute resources effectively
  • 2. Controlling costs and securing storage
- 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 4: AI Software Architectures18%- 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
- Scaling and Orchestration
  • 1. Leveraging BlueXP software tools
  • 2. Scaling AI workloads with Kubernetes
Topic 5: AI Overview15%- Machine Learning Fundamentals
  • 1. Understand the relationship between AI, machine learning, and deep learning
  • 2. Describe machine learning benefits
- Training vs. Inferencing vs. Predictions
  • 1. Distinguish between training and inference workloads
- Algorithm Types
  • 1. Supervised learning
  • 2. Unsupervised learning
  • 3. Reinforcement learning
- AI Industry Applications
  • 1. Healthcare applications
  • 2. Agents
  • 3. Digital twins
- AI Deployment Models
  • 1. Cloud
  • 2. Edge
  • 3. On-premises
  • 4. Benefits and risks of each model
- AI Convergence with HPC and Analytics
  • 1. Leveraging shared infrastructure for AI, HPC, and analytics

Network Appliance NetApp Certified AI Expert Sample Questions:

1. The data anonymization job, running in a Kubernetes pod, fails. The pod logs show a "Permission Denied" error when trying to access the source volume on the on-premises ASA. An administrator checks the export policy rule for the volume.
The active rule is as follows:
Rule_Index: 1
Client_Match: 10.50.0.0/16
Protocols: nfs4
Read_Only_Access: sys
Read_Write_Access: -
Superuser_Access: none
The Kubernetes pod that failed has an IP address of '10.60.5.10'.
What is the cause of the "Permission Denied" error?

A) The export policy does not grant read-write access, which is required by the anonymization job.
B) The 'Superuser_Access' setting is too restrictive.
C) The export policy only allows access via the NFSv4 protocol.
D) The pod's IP address ('10.60.5.10') is not within the allowed client match range ('10.50.0.0/16').


2. An automotive company runs crash simulations on a dedicated High-Performance Computing (HPC) cluster and trains computer vision models on a separate AI cluster. Data scientists are complaining about the long delays required to move terabytes of simulation output data from the HPC storage to the AI cluster's storage before they can begin training.
The current data flow is as follows:
HPC Cluster -> --Manual Copy (NFS)--> -> AI Cluster
An architect has been asked to redesign the infrastructure to eliminate this data movement bottleneck.
Which architectural change would be most effective?

A) Implement a converged data infrastructure where both the HPC and AI clusters access a single, high- performance data lake built on NetApp storage.
B) Use NetApp XCP to perform the data copy, as it is faster than a standard NFS copy.
C) Install faster CPUs in the AI cluster's storage controllers.
D) Upgrade the network connection between the two storage systems to 200GbE.


3. A new team of external auditors requires read-only access to the raw financial product documentation stored in the StorageGRID data lake. The data is in a bucket named 'prod-docs'.
The security team must ensure the auditors can list and read objects but cannot write, delete, or modify any data. Which is the most appropriate method to grant this specific access?

A) Provide the auditors with the root access keys for the StorageGRID system.
B) Configure an NFS export policy on the 'prod-docs' bucket and provide the auditors with the mount path.
C) Create a new group and user within StorageGRID, and attach an S3 group policy that explicitly allows only 's3:GetObject' and 's3:ListBucket' actions on the 'prod-docs' bucket.
D) Create a new StorageGRID tenant account for the auditors with full S3 access.


4. An MLOps team uses a variety of platforms to manage their AI workloads. They need to understand the primary function of each tool within their ecosystem. Which statement best describes the role of an MLOps/LLMOps platform like Kubeflow or Run:AI?

A) They are orchestration and management platforms that automate and streamline the entire AI/ML lifecycle, from data preparation and model training to deployment and monitoring.
B) They are specialized storage systems designed to hold large datasets for training.
C) They are networking protocols designed to accelerate data transfer between GPUs.
D) They are integrated development environments (IDEs) used exclusively for writing Python code.


5. An architect is designing a comprehensive AI platform for a large enterprise. The platform must support the entire data lifecycle, from ingest at the edge to a central data lake, and finally to a high- performance training cluster.
The requirements are:
- Edge Ingest: Data must be collected at remote sites and efficiently replicated to the core.
- Data Lake: A central, petabyte-scale repository for unstructured data, accessible via the S3 protocol.
- Training Cluster: A high-performance compute cluster that requires low-latency, parallel file access to training datasets.
- Data Traceability: All datasets used for training must be immutably versioned.
Which combination of NetApp technologies and protocols should the architect choose to build this solution? (Select all that apply.)

A) Use NetApp Snapshots on the training dataset volumes to create immutable, point-in-time versions for traceability.
B) Use NetApp FlexCache to tier cold data from the data lake to the public cloud.
C) Use NetApp StorageGRID to create the petabyte-scale, S3-accessible data lake at the core.
D) Use NetApp ONTAP systems at the edge and NetApp SnapMirror to replicate data to the core data center.
E) Use iSCSI as the primary protocol for the data lake to ensure maximum compatibility.
F) Use a NetApp E-Series system with a parallel file system (like BeeGFS) to provide high- performance, parallel file access for the training cluster.


Solutions:

Question # 1
Answer: D
Question # 2
Answer: A
Question # 3
Answer: C
Question # 4
Answer: A
Question # 5
Answer: A,C,D,F

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