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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Security, Reliability, and Operations	15%	- High availability and data protection - Cost management and efficiency - Monitoring, logging, and troubleshooting AI environments - Data security and access control for AI
AI Lifecycle	27%	- AI governance, ethics, and compliance - Data preparation and management for AI - Model training, inference, and optimization - Predictive vs generative AI - AI lifecycle stages: design, training, deployment, monitoring
AI Overview	15%	- Algorithm types: supervised, unsupervised, reinforcement learning - AI industry use cases and applications - AI deployment models: on-premises, cloud, edge - Convergence of AI, high-performance computing, and analytics - AI, machine learning, and deep learning concepts
Cloud and Hybrid Cloud AI Deployment	18%	- Cloud-native AI solutions and integration - NetApp cloud data services for AI - Hybrid and multi-cloud AI architectures - Data mobility and consistency across environments
NetApp AI Solutions and Architecture	25%	- NetApp AI-ready infrastructure components - Data management and data pipeline design - Storage architectures for AI workloads - ONTAP integration with AI frameworks - Scalability and performance optimization for AI

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q64-Q69):

NEW QUESTION # 64
A media company is building a new generative AI service. The project has two main components:
1. Data Lake & Fine-Tuning: A 300 TB repository of unstructured data (videos, images, text) stored as objects will be used to fine-tune a foundational model. This process requires a scalable, cost-effective storage solution that can integrate with cloud-native data processing tools like Apache Spark.
2. Inference & RAG: The fine-tuned model will be used in a customer-facing application that leverages Retrieval-Augmented Generation (RAG). To ensure low-latency responses, the RAG component requires extremely fast lookups from a 10 TB vector database.
The company needs a solution that optimizes both cost and performance for this entire lifecycle.
Which combination of NetApp technologies provides the most appropriate solution for this scenario?

A. Use NetApp Cloud Volumes ONTAP for the data lake and NetApp StorageGRID for the vector database.
B. Use NetApp StorageGRID for the data lake and a NetApp ASA system for the vector database.
C. Use NetApp E-Series for the data lake and a NetApp ASA system for the vector database.
D. Use a single, large NetApp ASA system for both the object data lake and the vector database.

Answer: B

NEW QUESTION # 65
The company decides to establish a disaster recovery (DR) site in a secondary data center for the entire Digital Twin platform. The DR plan must protect the HPC data, the AI training data, and the central data lake.
The DR requirements are:
- RPO: 4 hours for all data.
- RTO: 24 hours for the entire platform.
- Process: The failover and failback process should be as automated as possible.
Which combination of technologies provides the most comprehensive DR solution?

A. Use BlueXP backup and recovery to back up all three storage systems (ASA, E-Series, StorageGRID) to a single cloud target.
B. Use NetApp SnapMirror for the ASA, StorageGRID replication for the data lake, and rely on tape backups for the E-Series/BeeGFS data.
C. Deploy a MetroCluster configuration spanning the primary and DR sites for all three storage systems.
D. Use NetApp SnapMirror for the ASA, StorageGRID's built-in replication for the data lake, and a host- based tool like 'rsync' for the E-Series/BeeGFS data, all orchestrated via BlueXP disaster recovery.

Answer: D

NEW QUESTION # 66
Which of the following are typical requirements for inferencing in AI models? (Choose two)

A. Pre-trained models
B. Cloud-based inference only
C. Loading the model into memory
D. Extensive training data

Answer: A,C

NEW QUESTION # 67
An AI operations team is troubleshooting why their RAG-based chatbot is providing outdated information. They have confirmed that the vector database embedding process is functioning correctly, but suspect an issue with the initial data synchronization that moves the knowledge base from an on- premises ONTAP file share to a cloud staging bucket.
They inspect the relevant BlueXP copy and sync job and find the following details:
Service: BlueXP copy and sync
Relationship_Name: KB_Sync_to_Vector_Staging
Source: nfs://ontap-cluster-1/vol_kb/docs
Destination: s3://vector-staging-bucket-89a3/latest/
Last_Sync_Status: FAILED
Last_Sync_Time: 2025-07-11T02:00:15Z
Error_Message: "Authentication error:
Unable to access source.
Check export policy on 'vol_kb'."
Based on this information, what is the most direct solution to fix the data pipeline?

A. Modify the NFS export policy on the 'vol_kb' volume on the on-premises ONTAP cluster to grant access to the BlueXP Connector.
B. Check the IAM permissions for the role associated with the S3 bucket.
C. Fine-tune the LLM with the latest data instead of using the RAG system.
D. Re-run the vector database embedding job.

Answer: A

NEW QUESTION # 68
An MLOps team uses a variety of platforms to manage their AI workloads. They need to understand the primary function of each tool within their ecosystem. Which statement best describes the role of an MLOps/LLMOps platform like Kubeflow or Run:AI?

A. They are networking protocols designed to accelerate data transfer between GPUs.
B. They are orchestration and management platforms that automate and streamline the entire AI/ML lifecycle, from data preparation and model training to deployment and monitoring.
C. They are specialized storage systems designed to hold large datasets for training.
D. They are integrated development environments (IDEs) used exclusively for writing Python code.

Answer: B

NEW QUESTION # 69
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