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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q61-Q66):

NEW QUESTION # 61
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 BlueXP Connector deployed in a public subnet with a public IP address to allow access to the LLM.
C. A policy in BlueXP classification to copy all sensitive data to a public cloud bucket for easier access.
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 direct VPN connection from each data scientist's laptop to the public LLM provider.

Answer: A,D

NEW QUESTION # 62
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 exclusive use of public cloud resources for all computational tasks.
B. The convergence of AI, High-Performance Computing (HPC), and analytics on a unified data infrastructure.
C. The replacement of all physical testing with digital simulations.
D. The separation of AI and HPC into dedicated, air-gapped environments.

Answer: B

NEW QUESTION # 63
An architect is designing a data pipeline for a predictive AI model that will forecast retail sales.
The pipeline must be robust, version-controlled, and efficient.
The proposed data flow is as follows:
1. Ingest: Raw sales data is copied daily from multiple point-of-sale (POS) systems to a central staging area on an on-premises ONTAP cluster.
2. Prepare: The raw data is messy. A data engineering team needs a clean, isolated, and writable copy of the latest daily data to perform cleansing and feature engineering tasks without impacting the original raw data.
3. Train: Once prepared, the cleansed dataset is used to retrain the predictive model on a GPU cluster.
This step must be repeatable with the exact same dataset for compliance.
4. Deploy: The newly trained model is pushed to production inference servers.
Which combination of NetApp technologies best supports this entire predictive AI lifecycle?
(Select all
that apply.)

A. Use NetApp FlexClone to create an instantaneous, space-efficient, writable copy of the daily raw data for the data preparation stage.
B. Use NetApp StorageGRID as the primary storage for the high-performance training stage.
C. Use NetApp XCP to efficiently aggregate the raw sales data from POS systems into the central staging area.
D. Use a RAG architecture for the sales forecasting model.
E. Use BlueXP backup and recovery to perform the initial data ingest from the POS systems.
F. Use NetApp Snapshots on the prepared dataset volume just before training to create an immutable, point-in-time version for compliance and reproducibility.

Answer: A,C,F

NEW QUESTION # 64
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 SnapMirror to replicate the raw, sensitive data to Azure first, then run BlueXP classification in the cloud to find PII.
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 FabricPool to automatically tier all raw data to Azure Blob storage, then mount the blob container to the training VMs.

Answer: C

NEW QUESTION # 65
The firm decides to expand the "Advisor Assistant" project to a new team in a different department. This team needs its own isolated environment. An MLOps engineer attempts to submit a new GPU- intensive job for the new team, but it remains pending. The engineer checks the Run:AI scheduler logs and finds the following entry:
time="2025-07-11T16:45:00Z" level=info msg="Job ds-new-team-job1 cannot be scheduled.
Project 'new-team-project' has exceeded its GPU quota. Quota: 0, Requested: 1, Used: 0." What is the root cause of the scheduling failure?

A. The Kubernetes cluster has no available GPUs.
B. The Run:AI scheduler is offline and cannot process new jobs.
C. A Run:AI project quota has been configured for the new team, but it has been set to zero, effectively blocking them from using any GPU resources.
D. The job is requesting a specific type of GPU that is not available in the cluster.

Answer: C

NEW QUESTION # 66
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