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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Common Challenges	22%	- Resource Management 1. Controlling costs and securing storage 2. Sizing storage and compute resources effectively - Traceability and Optimization 1. Optimizing data access and movement 2. Ensuring traceability for code, data, and models 3. Maximizing performance in demanding AI workloads
Topic 2: AI Software Architectures	18%	- Development Tools 1. NetApp DataOps Toolkit 2. Jupyter notebooks vs. pipelines - MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale - Scaling and Orchestration 1. Scaling AI workloads with Kubernetes 2. Leveraging BlueXP software tools
Topic 3: AI Overview	15%	- Machine Learning Fundamentals 1. Understand the relationship between AI, machine learning, and deep learning 2. Describe machine learning benefits - Algorithm Types 1. Unsupervised learning 2. Reinforcement learning 3. Supervised learning - AI Deployment Models 1. Cloud 2. Benefits and risks of each model 3. Edge 4. On-premises - 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 Industry Applications 1. Healthcare applications 2. Agents 3. Digital twins
Topic 4: AI Lifecycle	27%	- Data Preparation 1. XCP and CopySync 2. Data aggregation and cleansing 3. NetApp BlueXP Classification - Model Development 1. Model building 2. Inferencing 3. Fine-tuning workflows - Generative AI Concepts 1. Fine-tuning 2. Retrieval Augmented Generation (RAG) 3. Hallucinations - 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)
Topic 5: AI Hardware Architectures	18%	- NetApp Architectures 1. SuperPOD 2. OVX architectures 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 (Q55-Q60):

NEW QUESTION # 55
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 pre-trained model, a validation set, an inference engine, and a cloud provider.
B. A project manager, a data scientist, a software engineer, and a budget.
C. Data, code, compute, and time.
D. A data lake, a data warehouse, a data pipeline, and a data mart.

Answer: C

NEW QUESTION # 56
An AI architect is planning the resource allocation for a new project. The primary task is to process millions of unlabeled customer reviews to identify naturally occurring groups or themes without any prior guidance.
The project requirements are summarized below:
Task: Discover hidden patterns in text data
Input_Data: 10 million unlabeled text reviews
Output: Clustered groups of related reviews
Supervision: None
Which type of machine learning algorithm is required for this task?

A. Unsupervised learning
B. Reinforcement learning
C. Supervised learning
D. Predictive learning

Answer: A

NEW QUESTION # 57
An AI operations team is troubleshooting why their RAG-based chatbot is providing outdated information. They have confirmed that the vector database embedding process is functioning correctly, but suspect an issue with the initial data synchronization that moves the knowledge base from an on- premises ONTAP file share to a cloud staging bucket.
They inspect the relevant BlueXP copy and sync job and find the following details:
Service: BlueXP copy and sync
Relationship_Name: KB_Sync_to_Vector_Staging
Source: nfs://ontap-cluster-1/vol_kb/docs
Destination: s3://vector-staging-bucket-89a3/latest/
Last_Sync_Status: FAILED
Last_Sync_Time: 2025-07-11T02:00:15Z
Error_Message: "Authentication error:
Unable to access source.
Check export policy on 'vol_kb'."
Based on this information, what is the most direct solution to fix the data pipeline?

A. Modify the NFS export policy on the 'vol_kb' volume on the on-premises ONTAP cluster to grant access to the BlueXP Connector.
B. Fine-tune the LLM with the latest data instead of using the RAG system.
C. Check the IAM permissions for the role associated with the S3 bucket.
D. Re-run the vector database embedding job.

Answer: A

NEW QUESTION # 58
An AI architect is designing a solution for a legal firm. The primary goal is to allow lawyers to ask natural language questions about case law stored in a private, 50 TB document repository.
The key project constraints are as follows:
Project_Goal: Answer questions using proprietary, real-time legal documents.
Constraint_1: Must not alter the foundational LLM's weights due to compliance.
Constraint_2: Case law database is updated daily with new rulings.
Constraint_3: All generated answers must be traceable to a source document.
Which technology should the architect choose as the core of this solution?

A. A fine-tuning pipeline to update the LLM daily.
B. A Retrieval-Augmented Generation (RAG) architecture.
C. A predictive AI model to classify legal documents.
D. A new LLM trained from scratch on the legal documents.

Answer: B

NEW QUESTION # 59
Which of the following describes the impact of generative AI in content creation?

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

Answer: C

NEW QUESTION # 60
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