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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Lifecycle	27%	- 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) - Generative AI Concepts 1. Fine-tuning 2. Hallucinations 3. Retrieval Augmented Generation (RAG) - Data Preparation 1. XCP and CopySync 2. NetApp BlueXP Classification 3. Data aggregation and cleansing - Model Development 1. Inferencing 2. Fine-tuning workflows 3. Model building
AI Common Challenges	22%	- Resource Management 1. Controlling costs and securing storage 2. Sizing storage and compute resources effectively - Traceability and Optimization 1. Ensuring traceability for code, data, and models 2. Maximizing performance in demanding AI workloads 3. Optimizing data access and movement
AI Software Architectures	18%	- MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale - Scaling and Orchestration 1. Leveraging BlueXP software tools 2. Scaling AI workloads with Kubernetes - Development Tools 1. Jupyter notebooks vs. pipelines 2. NetApp DataOps Toolkit
AI Overview	15%	- AI Deployment Models 1. Benefits and risks of each model 2. Cloud 3. On-premises 4. Edge - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - Algorithm Types 1. Supervised learning 2. Reinforcement learning 3. Unsupervised learning - AI Industry Applications 1. Agents 2. Healthcare applications 3. Digital twins - Machine Learning Fundamentals 1. Describe machine learning benefits 2. Understand the relationship between AI, machine learning, and deep learning - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics
AI Hardware Architectures	18%	- Infrastructure Topologies 1. Data aggregation and compute topologies - NetApp Architectures 1. OVX architectures 2. BasePod 3. SuperPOD - 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 (Q13-Q18):

NEW QUESTION # 13
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 Retrieval-Augmented Generation (RAG) architecture.
B. A predictive AI model to classify legal documents.
C. A new LLM trained from scratch on the legal documents.
D. A fine-tuning pipeline to update the LLM daily.

Answer: A

NEW QUESTION # 14
The firm wants to extend the "Advisor Assistant" to include a new batch processing feature. Every night, the system must analyze every client portfolio against a set of 50 different risk models and generate a compliance report. This is a highly parallel, read-intensive workload. The architect must design a data workflow that is efficient and does not impact the production chatbot environment. Which sequence of actions and technologies provides the most effective solution?

A. Use NetApp SnapMirror to replicate the portfolio database volume to the DR site, and run the analysis jobs there.
B. Create a NetApp Snapshot of the portfolio database volume, create a FlexClone from that snapshot, mount the FlexClone to the analysis pods, and run the batch job.
C. Create a full physical copy of the client portfolio database to a separate volume, mount it to the compute nodes, and run the analysis.
D. Run the analysis job directly against the production portfolio database during off-peak hours.

Answer: B

NEW QUESTION # 15
An organization wants to provide its data science team with a secure, on-demand method for using a powerful generative AI model with their private, sensitive corporate data. The solution must ensure that the private data is never exposed to the public internet or the public LLM API endpoint.
The architect is designing a solution using BlueXP.
Which two components are essential for building this secure solution? (Choose 2.)

A. A private vector database hosted on an on-premises NetApp ONTAP system to store embeddings of the sensitive corporate data.
B. A policy in BlueXP classification to copy all sensitive data to a public cloud bucket for easier access.
C. A BlueXP Connector deployed in a public subnet with a public IP address to allow access to the LLM.
D. A direct VPN connection from each data scientist's laptop to the public LLM provider.
E. The BlueXP GenAI Toolkit, which acts as a proxy to intercept user prompts and enrich them with data from a local vector database.

Answer: A,E

NEW QUESTION # 16
An organization is planning to deploy a large AI infrastructure but wants to avoid a large, upfront capital expenditure. They prefer an operational expenditure (OpEx) model where they pay for storage and compute resources as they are consumed. They also need the flexibility to scale resources up or down based on project demands.
Which NetApp consumption model is specifically designed to meet these financial and operational requirements?

A. NetApp Keystone, which provides a subscription-based, pay-as-you-go service for on-premises and cloud storage.
B. A perpetual licensing model for all ONTAP software features.
C. NetApp Cloud Volumes ONTAP PAYGO licensing only.
D. A standard capital purchase of AFF and ASA systems.

Answer: A

NEW QUESTION # 17
An AI architect is reviewing the design for a new data lake. The primary requirement is to store petabytes of unstructured data (images, video, sensor logs) in a highly durable, scalable, and cost- effective manner. The data will be accessed via S3 API by various data processing and analytics applications.
The initial design proposes using a traditional Network Attached Storage (NAS) filer with a large number of disks. The architect reviews the proposal:
Proposed_System: Traditional NAS Filer
Protocol: NFSv4
Scalability_Model: Scale-up
Metadata_Handling: Centralized in filer head
Cost_per_GB: Moderate
Why is this proposed system a poor choice for a petabyte-scale data lake?

A. A NAS filer cannot be deployed on-premises.
B. A traditional scale-up NAS system will face scalability and cost-effectiveness challenges at the petabyte scale compared to an object storage system.
C. NFS is incapable of storing image or video files.
D. The S3 API cannot be used to access data stored on an NFS file system.

Answer: B

NEW QUESTION # 18
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