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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q19-Q24):

NEW QUESTION # 19
Which of the following best describes the difference between data lakes, data warehouses, and lakehouses?

A. Data lakes store data in cloud storage, data warehouses store it in traditional databases, and lakehouses store it in external drives.
B. Data lakes store structured data, data warehouses store unstructured data, and lakehouses store only real-time data.
C. Data lakes store raw, unstructured data, data warehouses store structured data, and lakehouses combine the features of both.
D. Data lakes store metadata, data warehouses store transaction data, and lakehouses store archival data.

Answer: C

NEW QUESTION # 20
An AI architect needs to design a complete, end-to-end data pipeline for a new generative AI application at a financial services firm. The application will allow internal analysts to query a massive, 500 TB archive of historical market data and reports to generate summaries. The firm has the following environment and requirements:
Data_Sources: A mix of on-premises ONTAP filers and StorageGRID S3 buckets.
Requirement_1: All queries must be answered using only the private data archive.
Requirement_2: All generated summaries must provide citations to the source reports.
Requirement_3: All data containing client PII must be identified and excluded from the LLM context.
Requirement_4: The solution must be cost-effective for the large, mostly-read data archive.
Which set of actions and technologies constitutes the most robust and compliant solution?
(Select all that apply.)

A. During the RAG retrieval step, filter out any documents tagged as containing PII by BlueXP classification before sending them to the LLM.
B. Implement a Retrieval-Augmented Generation (RAG) architecture to meet the requirements for private data usage and source citation.
C. Deploy BlueXP classification to scan the entire StorageGRID data lake to identify and tag all files containing PII.
D. Use NetApp XCP to perform a one-time migration of all data from the ONTAP filers to the StorageGRID data lake.
E. Use SnapMirror to replicate the StorageGRID data lake to a high-performance NetApp ASA system for faster query performance.
F. Fine-tune a foundation model on the entire 500 TB dataset to ensure it understands the financial context.

Answer: A,B,C,D

NEW QUESTION # 21
An architect is designing a global infrastructure for a company that develops AI for autonomous vehicles.
The design must accommodate three distinct locations and functions:
1. Edge (Test Tracks): Fleets of test cars generate 100s of TBs of sensor data per day. This data must be ingested locally with high performance.
2. Core (Primary Data Center): The raw data from all edge sites must be aggregated here. This location houses the primary data lake and the main GPU cluster for large-scale model training.
3. Cloud (Public Cloud Provider): Data scientists want to use cloud-native tools for experimental data processing and model development. They also need a cost-effective location for long-term archiving of raw data.
Which combination of deployment locations and NetApp technologies creates the most logical and efficient end-to-end solution?

A. Use NetApp ONTAP systems at the edge and core, and Cloud Volumes ONTAP in the public cloud.
Use SnapMirror to replicate data from edge to core, and FabricPool to tier data from the core to the cloud.
B. Deploy a single, global NetApp StorageGRID across all three locations to act as a unified data plane.
C. Use NetApp E-Series at the edge, NetApp ASA at the core, and NetApp StorageGRID in the cloud.
Use SnapMirror to move data between all three tiers.
D. Use Cloud Volumes ONTAP at the edge, NetApp StorageGRID at the core, and on-premises ONTAP for cloud archive.

Answer: A

NEW QUESTION # 22
A junior administrator is attempting to delete an old, unused Snapshot copy from a production volume to reclaim space but receives an error. The administrator is certain they are using the correct command and have the appropriate 'vsadmin' role.
The command and error are shown below:
cluster-1::> volume snapshot delete -vserver svm_prod -volume app_data -snapshot nightly.2025- 06-15_0015 Error: command failed: This operation is not permitted. Deletion of Snapshot copy "nightly.2025-
06-15_0015" on volume "app_data" in Vserver "svm_prod" requires approval.
Use the "security multi-admin-verify approval show" command to view pending approvals.
What is the most likely reason the administrator cannot delete the Snapshot?

A. The 'vsadmin' role does not have permission to delete Snapshots.
B. The Snapshot is locked by a SnapMirror relationship.
C. Multi-Admin Verification (MAV) is enabled, and the destructive operation requires approval from another designated administrator.
D. The volume is protected by SnapLock, and the Snapshot has not reached its expiry date.

Answer: C

NEW QUESTION # 23
Which of the following applications use AI in the healthcare industry? (Choose two)

A. Diagnosing diseases
B. Predicting patient outcomes
C. Managing hospital supply chains
D. Automating financial reporting

Answer: A,B

NEW QUESTION # 24
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