NetApp Certified AI Expert Exam brain dumps, NS0-901 dumps pdf

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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Overview	15%	- AI Industry Applications 1. Digital twins 2. Agents 3. Healthcare applications - AI Deployment Models 1. Benefits and risks of each model 2. Cloud 3. On-premises 4. Edge - Algorithm Types 1. Reinforcement learning 2. Unsupervised learning 3. Supervised learning - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - 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 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. Optimizing data access and movement 3. Maximizing performance in demanding AI workloads
AI Lifecycle	27%	- Generative AI Concepts 1. Hallucinations 2. Fine-tuning 3. Retrieval Augmented Generation (RAG) - Model Development 1. Model building 2. Inferencing 3. Fine-tuning workflows - 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 - Data Preparation 1. Data aggregation and cleansing 2. NetApp BlueXP Classification 3. XCP and CopySync
AI Software Architectures	18%	- 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 - MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale
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. Storage architectures for AI 2. Network protocols for AI workloads

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

NEW QUESTION # 19
Given the company's goal of combining physics-based simulations with AI-driven analytics on a shared data foundation, which industry trend does this project best represent?

A. The separation of AI and HPC into dedicated, air-gapped environments.
B. The exclusive use of public cloud resources for all computational tasks.
C. The replacement of all physical testing with digital simulations.
D. The convergence of AI, High-Performance Computing (HPC), and analytics on a unified data infrastructure.

Answer: D

NEW QUESTION # 20
A network administrator, attempting to harden the security of the data center, modifies a firewall access control list (ACL). Immediately afterward, the "Advisor Assistant" application pods can no longer mount their required NFS volumes from the AFF A-Series. The MLOps team confirms the pods are stuck in a 'ContainerCreating' state with a 'MountVolume.SetUp failed... connection timed out' error. The administrator provides the new, active firewall rule:
RULE | ACTION | PROTOCOL | SOURCE_IP_RANGE | DEST_IP_RANGE | DEST_PORT --|--|-
|--||--51 | ALLOW | TCP | 10.20.5.0/24 | 10.20.10.0/24 | 2049
What is the most likely reason for the mount failures?

A. The firewall rule is blocking the NFS lock manager (NLM) protocol, which is required for file locking.
B. The firewall rule is blocking the NFSv4 protocol, which requires TCP port 2050.
C. The 'SOURCE_IP_RANGE' is incorrect and does not include the Kubernetes pod IP addresses.
D. The firewall rule is blocking the Portmapper/RPCbind service (TCP/UDP port 111), which is necessary for the initial NFS mount negotiation.

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

Answer: C

NEW QUESTION # 22
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. NetApp FabricPool to automatically tier the data from StorageGRID to the ASA.
B. A custom Python script using the NetApp DataOps Toolkit to trigger the data movement.
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. NetApp SnapMirror to replicate data from the StorageGRID object store to the ASA's file system.
E. A manual copy process using an S3 client on a bastion host.

Answer: B,C

NEW QUESTION # 23
To meet HIPAA compliance, the first step in the data pipeline is to identify all medical scans that contain embedded PII. The solution must be automated and capable of scanning data in-place on the on-premises ASA system.
Which two technologies should be used to accomplish this identification and tagging task?
(Choose 2.)

A. A manual review process where technicians visually inspect each scan for PII.
B. A custom "PII" category within BlueXP classification to identify specific medical record number formats.
C. NetApp BlueXP classification, configured to scan the on-premises ASA working environment.
D. NetApp SnapLock to make the source data immutable before scanning.
E. A custom Python script that uses regular expressions to search file contents.

Answer: B,C

NEW QUESTION # 24
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