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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Common Challenges	22%	- 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
AI Software Architectures	18%	- MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale - Development Tools 1. NetApp DataOps Toolkit 2. Jupyter notebooks vs. pipelines - Scaling and Orchestration 1. Scaling AI workloads with Kubernetes 2. Leveraging BlueXP software tools
AI Lifecycle	27%	- Data Preparation 1. NetApp BlueXP Classification 2. Data aggregation and cleansing 3. XCP and CopySync - Generative AI Concepts 1. Retrieval Augmented Generation (RAG) 2. Fine-tuning 3. Hallucinations - Predictive AI vs. Generative AI 1. Large Language Models (LLMs) 2. Distinction between predictive and generative AI 3. Impact of generative content (text, images, video, decision-making) - Model Development 1. Inferencing 2. Model building 3. Fine-tuning workflows
AI Overview	15%	- Algorithm Types 1. Unsupervised learning 2. Reinforcement learning 3. Supervised learning - AI Industry Applications 1. Healthcare applications 2. Agents 3. Digital twins - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - Machine Learning Fundamentals 1. Describe machine learning benefits 2. Understand the relationship between AI, machine learning, and deep learning - AI Deployment Models 1. Edge 2. Benefits and risks of each model 3. On-premises 4. Cloud - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads
AI Hardware Architectures	18%	- Infrastructure Topologies 1. Data aggregation and compute topologies - NetApp Architectures 1. OVX architectures 2. SuperPOD 3. BasePod - 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 (Q49-Q54):

A. Use RAG exclusively, as prompt engineering alone can fully replicate a specific brand voice and persona.
B. Recommend training a new LLM from scratch to ensure both brand voice and factual accuracy are built-in.
C. Prioritize fine-tuning to embed the company's brand voice and persona into the model.
D. Propose a hybrid approach where a base model is first lightly fine-tuned for persona, then used within a RAG system for factual grounding.
E. Prioritize a RAG architecture to meet the critical requirements for factual accuracy and data traceability.

Answer: D,E

NEW QUESTION # 50
The data scientists report that their Kubernetes-based data preparation jobs are failing. The pods are stuck in a 'Pending' state.
An MLOps engineer runs 'kubectl describe pvc data-prep-pvc-01' and sees the following event:
Type Reason Age From Message
- - - -
Warning ProvisioningFailed 2m15s trident-orchestrator-7b... failed to provision volume with StorageClass "bronze-tier": backend unavailable: no healthy backend with satisfying attributes for storage class "bronze-tier" The engineer checks the Trident backend configurations and finds no backend associated with the "bronze-tier" StorageClass.
What is the root cause of the failure?

A. The PersistentVolumeClaim is referencing a StorageClass that has no configured or healthy Trident backend to provision storage from.
B. The Kubernetes scheduler is unable to find a node with enough resources for the pods.
C. The data preparation pods do not have the correct security permissions.
D. The StorageGRID data lake is offline, preventing the creation of new volumes.

Answer: A

NEW QUESTION # 51
The architect needs to design an efficient data flow to move curated training sets from the central StorageGRID data lake to the high-performance NetApp ASA system used by the AI cluster. The process must be manageable from a single interface and should be automatable. Which two NetApp technologies should be used to implement this data pipeline stage? (Choose 2.)

A. A custom Python script using the NetApp DataOps Toolkit to trigger the data movement.
B. NetApp BlueXP copy and sync service to create and manage the data synchronization relationship between the S3 source and the NFS/CIFS destination.
C. NetApp SnapMirror to replicate data from the StorageGRID object store to the ASA's file system.
D. NetApp FabricPool to automatically tier the data from StorageGRID to the ASA.
E. A manual copy process using an S3 client on a bastion host.

Answer: A,B

NEW QUESTION # 52
Which of the following techniques are used to maximize GPU utilization in AI workloads? (Choose two)

A. Utilizing CPU-based workloads for all tasks
B. Balancing compute and storage workloads efficiently
C. Using larger batch sizes
D. Scheduling GPU-intensive tasks during off-peak hours

Answer: B,D

NEW QUESTION # 53
A development team is building a generative AI application that must answer questions based on a constantly changing internal knowledge base of company documents. They want to provide the model with up-to-date information without altering its core weights and capabilities.
Which approach is most suitable for this requirement?

A. Unsupervised learning, which clusters the documents into topics for the model to browse.
B. Fine-tuning, which retrains the model's last few layers on the new documents.
C. Retrieval-Augmented Generation (RAG), which fetches relevant information from the documents at query time to inform the model's response.
D. Predictive AI, which analyzes the documents to forecast future questions.

Answer: C

NEW QUESTION # 54
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