NS0-901 Hot Questions & NS0-901 Valid Exam Vce

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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
AI and Machine Learning Fundamentals	- Algorithm types 1. Supervised learning 2. Reinforcement learning 3. Unsupervised learning - AI, ML, DL concepts 1. Differences between AI, machine learning, and deep learning 2. Training, inference, and prediction workflows
AI Lifecycle and Deployment	- Operational challenges 1. Data governance and management 2. Scalability and performance optimization - End-to-end AI lifecycle 1. Model deployment and monitoring 2. Data ingestion and preparation 3. Model training and validation
Industry Use Cases	- AI applications across industries 1. Digital twins 2. Autonomous systems and agents 3. Healthcare AI
AI Infrastructure and NetApp Solutions	- AI-ready data infrastructure 1. Data pipeline optimization for AI/ML workloads 2. High-performance storage for AI workloads - Converged workloads 1. Shared infrastructure design considerations 2. AI, HPC, and analytics convergence

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q67-Q72):

NEW QUESTION # 67
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 custom Python script using the NetApp DataOps Toolkit to trigger the data movement.
B. NetApp BlueXP copy and sync service to create and manage the data synchronization relationship between the S3 source and the NFS/CIFS destination.
C. NetApp FabricPool to automatically tier the data from StorageGRID to the ASA.
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: A,B

NEW QUESTION # 68
A pharmaceutical company is creating a "digital twin" of its manufacturing process. This involves running complex simulations (an HPC workload) that generate massive datasets.
The company wants to use this data immediately for two other purposes:
1. Analytics: Business analysts need to run complex queries on the simulation output using tools like Spark.
2. AI Training: Data scientists need to use the same output as a training set for a predictive maintenance model.
The company wants to avoid creating separate data silos for each workload.
Which two NetApp technologies are best suited for building a unified data lake that can efficiently serve all three workloads (HPC, Analytics, AI)? (Choose 2.)

A. NetApp StorageGRID to provide a scalable, S3-native object store that integrates directly with modern analytics platforms like Spark.
B. NetApp Keystone to provide a flexible, pay-as-you-go consumption model for the infrastructure.
C. NetApp ONTAP with FlexGroup volumes to provide high-throughput, parallel NFS access for the HPC and AI training workloads.
D. NetApp SnapCenter to create application-consistent backups of the data.
E. NetApp Autonomous Ransomware Protection to secure the data from modification.

Answer: A,C

NEW QUESTION # 69
A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
Which tools are best suited for each of these roles?

A. The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).
B. The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.
C. Both the data scientist and the DevOps engineer should use automated production pipelines.
D. Both the data scientist and the DevOps engineer should use Jupyter Notebooks.

Answer: A

NEW QUESTION # 70
An AI platform team is investigating poor I/O performance for a specific workload that involves processing hundreds of thousands of small metadata files. The application is running on a Kubernetes cluster with storage provided by a NetApp ONTAP system over NFS. Performance metrics show acceptable network throughput but very high latency for metadata operations (e.g., open, stat, close).
The current storage configuration is as follows:
Storage_System: NetApp AFF A-Series
Protocol: NFSv4.1
Workload_Profile: Metadata-intensive, many small file lookups
Observed_Issue: High latency on metadata operations, slow job completion Which storage architecture would be better suited to handle this specific metadata-intensive workload?

A. A NetApp SnapLock-enabled volume for data protection.
B. A NetApp StorageGRID object storage system.
C. A NetApp Cloud Volumes ONTAP instance in a different region.
D. A NetApp E-Series system running a parallel file system like BeeGFS.

Answer: D

NEW QUESTION # 71
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 direct VPN connection from each data scientist's laptop to the public LLM provider.
B. A BlueXP Connector deployed in a public subnet with a public IP address to allow access to the LLM.
C. A private vector database hosted on an on-premises NetApp ONTAP system to store embeddings of the sensitive corporate data.
D. The BlueXP GenAI Toolkit, which acts as a proxy to intercept user prompts and enrich them with data from a local vector database.
E. A policy in BlueXP classification to copy all sensitive data to a public cloud bucket for easier access.

Answer: C,D

NEW QUESTION # 72
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