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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Overview	15%	- AI Deployment Models 1. Benefits and risks of each model 2. Cloud 3. Edge 4. On-premises - AI Industry Applications 1. Agents 2. Digital twins 3. Healthcare applications - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - 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. Supervised learning 3. Reinforcement learning - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads
Topic 2: AI Hardware Architectures	18%	- NetApp Architectures 1. SuperPOD 2. BasePod 3. OVX architectures - Networking and Storage 1. Storage architectures for AI 2. Network protocols for AI workloads - Infrastructure Topologies 1. Data aggregation and compute topologies
Topic 3: 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 4: AI Common Challenges	22%	- Resource Management 1. Sizing storage and compute resources effectively 2. Controlling costs and securing storage - 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 5: AI Lifecycle	27%	- Data Preparation 1. NetApp BlueXP Classification 2. XCP and CopySync 3. Data aggregation and cleansing - Generative AI Concepts 1. Hallucinations 2. Retrieval Augmented Generation (RAG) 3. Fine-tuning - Predictive AI vs. Generative AI 1. Impact of generative content (text, images, video, decision-making) 2. Large Language Models (LLMs) 3. Distinction between predictive and generative AI - Model Development 1. Inferencing 2. Fine-tuning workflows 3. Model building

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q86-Q91):

NEW QUESTION # 86
Which storage protocols are commonly used for handling large-scale AI data? (Choose two)

A. Parallel file systems
B. File-based systems
C. Object-based storage
D. POSIX-based file systems

Answer: A,C

NEW QUESTION # 87
An architect is designing the storage and network infrastructure for a new, large-scale AI cluster dedicated to training foundational models. The primary design goal is to achieve the highest possible data throughput and the lowest latency to ensure multi-million dollar GPU resources are never idle. Which two technologies are essential to include in the design to achieve this goal?
(Choose 2.)

A. NetApp StorageGRID as the primary storage for the training datasets.
B. A tiered storage architecture using NetApp FabricPool.
C. A 10GbE Ethernet network for all data traffic.
D. GPUDirect Storage support on the storage system.
E. An InfiniBand or RoCE-capable Ethernet network fabric.

Answer: D,E

NEW QUESTION # 88
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 manual copy process using an S3 client on a bastion host.
B. NetApp FabricPool to automatically tier the data from StorageGRID to the ASA.
C. NetApp BlueXP copy and sync service to create and manage the data synchronization relationship between the S3 source and the NFS/CIFS destination.
D. A custom Python script using the NetApp DataOps Toolkit to trigger the data movement.
E. NetApp SnapMirror to replicate data from the StorageGRID object store to the ASA's file system.

Answer: C,D

NEW QUESTION # 89
A data scientist is using the NetApp DataOps Toolkit for Python to automate the creation of a new, writable volume for an experiment. The script is intended to clone an existing dataset volume. When the script is executed, it fails with an error.
The relevant portion of the Python script is:
from netapp_dataops.k8s import clone_pvc
clone_pvc(
source_pvc_name="dataset-v1-pvc",
new_pvc_name="experiment-clone-pvc",
namespace="ds-team-1"
)
The script produces the following error in the terminal:
'Error: Failed to clone PVC. Source PVC 'dataset-v1-pvc' not found in namespace 'ds-team-1'.' What is the most likely cause of this error?

A. The Kubernetes cluster does not have NetApp Trident installed.
B. The source PersistentVolumeClaim (PVC) named 'dataset-v1-pvc' does not exist or is in a different namespace.
C. The Python script is missing the necessary import statement for the toolkit.
D. The NetApp DataOps Toolkit does not support cloning volumes.

Answer: B

NEW QUESTION # 90
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. Implement a Retrieval-Augmented Generation (RAG) architecture to meet the requirements for private data usage and source citation.
B. Fine-tune a foundation model on the entire 500 TB dataset to ensure it understands the financial context.
C. During the RAG retrieval step, filter out any documents tagged as containing PII by BlueXP classification before sending them to the LLM.
D. Use SnapMirror to replicate the StorageGRID data lake to a high-performance NetApp ASA system for faster query performance.
E. Deploy BlueXP classification to scan the entire StorageGRID data lake to identify and tag all files containing PII.
F. Use NetApp XCP to perform a one-time migration of all data from the ONTAP filers to the StorageGRID data lake.

Answer: A,C,E,F

NEW QUESTION # 91
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