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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Topic 1: AI Infrastructure and NetApp Solutions	- Converged workloads 1. AI, HPC, and analytics convergence 2. Shared infrastructure design considerations - AI-ready data infrastructure 1. Data pipeline optimization for AI/ML workloads 2. High-performance storage for AI workloads
Topic 2: AI Lifecycle and Deployment	- Operational challenges 1. Scalability and performance optimization 2. Data governance and management - End-to-end AI lifecycle 1. Model training and validation 2. Model deployment and monitoring 3. Data ingestion and preparation
Topic 3: Industry Use Cases	- AI applications across industries 1. Healthcare AI 2. Autonomous systems and agents 3. Digital twins
Topic 4: AI and Machine Learning Fundamentals	- Algorithm types 1. Supervised learning 2. Unsupervised learning 3. Reinforcement learning - AI, ML, DL concepts 1. Training, inference, and prediction workflows 2. Differences between AI, machine learning, and deep learning

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q97-Q102):

NEW QUESTION # 97
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 private vector database hosted on an on-premises NetApp ONTAP system to store embeddings of the sensitive corporate data.
B. A policy in BlueXP classification to copy all sensitive data to a public cloud bucket for easier access.
C. The BlueXP GenAI Toolkit, which acts as a proxy to intercept user prompts and enrich them with data from a local vector database.
D. A BlueXP Connector deployed in a public subnet with a public IP address to allow access to the LLM.
E. A direct VPN connection from each data scientist's laptop to the public LLM provider.

Answer: A,C

NEW QUESTION # 98
Which of the following are typical requirements for inferencing in AI models? (Choose two)

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

Answer: A,B

NEW QUESTION # 99
The architect is designing the complete, automated data pipeline from the on-premises data center to the Azure cloud for this medical imaging project. The design must prioritize security, efficiency, and reproducibility.
Which sequence of actions provides the most robust and automated solution?

A. Use FabricPool to automatically tier all raw data to Azure Blob storage, then mount the blob container to the training VMs.
B. Manually copy raw data to Azure, run a cloud-based script to anonymize it, then start training.
C. Use BlueXP classification to scan the on-prem data; use a DataOps Toolkit script to trigger a FlexClone of the source volume for anonymization; use SnapMirror to replicate the anonymized clone to Cloud Volumes ONTAP; then begin training in Azure.
D. Use SnapMirror to replicate the raw, sensitive data to Azure first, then run BlueXP classification in the cloud to find PII.

Answer: C

NEW QUESTION # 100
The firm wants to extend the "Advisor Assistant" to include a new batch processing feature. Every night, the system must analyze every client portfolio against a set of 50 different risk models and generate a compliance report. This is a highly parallel, read-intensive workload. The architect must design a data workflow that is efficient and does not impact the production chatbot environment. Which sequence of actions and technologies provides the most effective solution?

A. Create a NetApp Snapshot of the portfolio database volume, create a FlexClone from that snapshot, mount the FlexClone to the analysis pods, and run the batch job.
B. Use NetApp SnapMirror to replicate the portfolio database volume to the DR site, and run the analysis jobs there.
C. Create a full physical copy of the client portfolio database to a separate volume, mount it to the compute nodes, and run the analysis.
D. Run the analysis job directly against the production portfolio database during off-peak hours.

Answer: A

NEW QUESTION # 101
The HPC cluster generates simulation data at an extremely high rate, requiring a storage system that can handle massively parallel writes from hundreds of compute nodes simultaneously. Which storage system and file protocol combination is the most appropriate choice for the HPC cluster's high-performance scratch space?

A. A NetApp StorageGRID system accessed via the S3 protocol.
B. A NetApp ASA system serving a single, large NFS volume.
C. A Cloud Volumes ONTAP instance with a standard file system.
D. A NetApp E-Series system serving a BeeGFS parallel file system.

Answer: D

NEW QUESTION # 102
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