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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Hardware Architectures	18%	- Infrastructure Topologies 1. Data aggregation and compute topologies - Networking and Storage 1. Storage architectures for AI 2. Network protocols for AI workloads - NetApp Architectures 1. SuperPOD 2. BasePod 3. OVX architectures
Topic 2: 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. Jupyter notebooks vs. pipelines 2. NetApp DataOps Toolkit
Topic 3: AI Overview	15%	- AI Industry Applications 1. Healthcare applications 2. Digital twins 3. Agents - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - Algorithm Types 1. Supervised learning 2. Reinforcement learning 3. Unsupervised learning - AI Deployment Models 1. On-premises 2. Benefits and risks of each model 3. Cloud 4. Edge - 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
Topic 4: AI Common Challenges	22%	- Traceability and Optimization 1. Ensuring traceability for code, data, and models 2. Optimizing data access and movement 3. Maximizing performance in demanding AI workloads - Resource Management 1. Controlling costs and securing storage 2. Sizing storage and compute resources effectively
Topic 5: AI Lifecycle	27%	- Model Development 1. Model building 2. Fine-tuning workflows 3. Inferencing - 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) - Data Preparation 1. Data aggregation and cleansing 2. NetApp BlueXP Classification 3. XCP and CopySync - Generative AI Concepts 1. Fine-tuning 2. Retrieval Augmented Generation (RAG) 3. Hallucinations

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q82-Q87):

NEW QUESTION # 82
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 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.
B. Use Cloud Volumes ONTAP at the edge, NetApp StorageGRID at the core, and on-premises ONTAP for cloud archive.
C. Deploy a single, global NetApp StorageGRID across all three locations to act as a unified data plane.
D. 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.

Answer: D

NEW QUESTION # 83
What is the primary architectural benefit of using technologies like RDMA (Remote Direct Memory Access) and GPUDirect Storage in a high-performance AI training cluster?

A. They reduce the power consumption of the storage array by offloading checksum calculations.
B. They enable the use of lower-cost Ethernet switches in place of InfiniBand fabrics without any performance degradation.
C. They allow GPUs to communicate directly with each other and with NVMe storage, bypassing the host CPU and system memory, which significantly reduces I/O latency and CPU overhead.
D. They automatically encrypt data in-flight between the GPU servers and the storage system.

Answer: C

NEW QUESTION # 84
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. Use NetApp XCP to perform a one-time migration of all data from the ONTAP filers to the StorageGRID data lake.
B. Fine-tune a foundation model on the entire 500 TB dataset to ensure it understands the financial context.
C. Implement a Retrieval-Augmented Generation (RAG) architecture to meet the requirements for private data usage and source citation.
D. Deploy BlueXP classification to scan the entire StorageGRID data lake to identify and tag all files containing PII.
E. Use SnapMirror to replicate the StorageGRID data lake to a high-performance NetApp ASA system for faster query performance.
F. During the RAG retrieval step, filter out any documents tagged as containing PII by BlueXP classification before sending them to the LLM.

Answer: A,C,D,F

NEW QUESTION # 85
An architect is explaining different data storage paradigms to a new data engineering team. The team needs to understand where to store different types of data for their AI projects. Which statement accurately describes a key difference between a data lake and a data warehouse?

A. A data lake is only for on-premises deployments, while a data warehouse is only for cloud deployments.
B. A data lake stores structured, processed data for fast querying, while a data warehouse stores raw, unstructured data in its native format.
C. A data lake uses a schema-on-write approach, while a data warehouse uses a schema-on-read approach.
D. A data lake is designed to store vast quantities of raw, multi-format data (structured, semi- structured, and unstructured), while a data warehouse is typically used for storing structured and filtered data that has already been processed for a specific purpose.

Answer: D

NEW QUESTION # 86
A research lab uses a fleet of autonomous drones to collect high-resolution aerial imagery for agricultural analysis. The drones land at a remote edge location and offload their data. The AI models for image analysis are trained at a central data center. The team is using NetApp SnapMirror to replicate the data from the edge to the core. However, the data scientists are complaining that the datasets arriving at the data center are often incomplete or corrupted.
An administrator reviews the SnapMirror configuration and status via the BlueXP API:
{
"source": { "workingEnvironmentId": "OnPrem-Edge-Filer-1", "volumeName": "drone_data_raw" },
"destination": { "workingEnvironmentId": "Core-Datacenter-A800", "volumeName":
"drone_data_replicated" },
"mirrorState": "broken-off",
"relationshipStatus": "idle",
"unhealthyReason": "Transfer failed. Destination volume is out of space.", "lastTransferInfo": {
"transferError": "No space left on device"
}
}
What is the direct cause of the incomplete datasets at the data center?

A. The network connection between the edge and the core is unreliable.
B. The SnapMirror relationship is broken because the destination volume at the core data center has run out of capacity.
C. The source volume at the edge location has become corrupted.
D. The BlueXP Connector does not have the correct permissions to manage the SnapMirror relationship.

Answer: B

NEW QUESTION # 87
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