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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Overview	15%	- Convergence of AI, high-performance computing, and analytics - AI deployment models: on-premises, cloud, edge - Algorithm types: supervised, unsupervised, reinforcement learning - AI, machine learning, and deep learning concepts - AI industry use cases and applications
Topic 2: AI Lifecycle	27%	- AI lifecycle stages: design, training, deployment, monitoring - Predictive vs generative AI - AI governance, ethics, and compliance - Model training, inference, and optimization - Data preparation and management for AI
Topic 3: NetApp AI Solutions and Architecture	25%	- Storage architectures for AI workloads - NetApp AI-ready infrastructure components - Scalability and performance optimization for AI - ONTAP integration with AI frameworks - Data management and data pipeline design
Topic 4: Security, Reliability, and Operations	15%	- Monitoring, logging, and troubleshooting AI environments - High availability and data protection - Cost management and efficiency - Data security and access control for AI
Topic 5: Cloud and Hybrid Cloud AI Deployment	18%	- Hybrid and multi-cloud AI architectures - Data mobility and consistency across environments - NetApp cloud data services for AI - Cloud-native AI solutions and integration

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
Given the firm's requirements for using a private, constantly updated knowledge base and the strict mandate for data traceability, which AI architecture is the most appropriate foundation for the "Advisor Assistant" chatbot?

A. A standalone LLM deployed in an air-gapped environment with no access to the document repository.
B. A predictive AI model trained to forecast market trends.
C. A Retrieval-Augmented Generation (RAG) architecture that retrieves relevant context from a local vector database to enrich prompts sent to the LLM.
D. A fine-tuning architecture where a base LLM is continuously retrained on the entire document repository.

Answer: C

NEW QUESTION # 24
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 direct VPN connection from each data scientist's laptop to the public LLM provider.
E. A BlueXP Connector deployed in a public subnet with a public IP address to allow access to the LLM.

Answer: A,C

NEW QUESTION # 25
The firm's data science team needs to run a high-priority, interactive model analysis job that requires immediate access to two GPUs. However, all GPUs in the cluster are currently allocated to long-running, lower-priority batch training jobs.
The MLOps platform, Run:AI, shows the following queue status:
JOB_ID | PROJECT | STATUS | PRIORITY | GPU_ALLOCATED
||--|-|
batch_job_1 | team_a | Running | Low | 2
batch_job_2 | team_a | Running | Low | 2
batch_job_3 | team_b | Running | Low | 4
interactive_1| team_c | Pending | High | 2 (requested)
How does the Run:AI platform address this resource contention to allow the high-priority job to run?

A. It automatically terminates all low-priority jobs to free up the entire cluster.
B. It sends an email notification to the administrator to manually reallocate the GPUs.
C. It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.
D. It automatically pauses one of the low-priority jobs, saves its state, and allocates its GPUs to the high- priority job, placing the paused job back in the queue.

Answer: D

NEW QUESTION # 26
An AI platform is suffering from poor performance during distributed training jobs. The training data resides on a single, large NFS volume. Monitoring shows that while the overall network throughput to the storage system is high, individual GPU nodes experience significant I/O wait times, and the single ONTAP volume is becoming a performance bottleneck. The goal is to re- architect the storage layout to maximize read parallelism and throughput for the training cluster.
Which two actions should the architect take to address this performance bottleneck? (Choose 2.)

A. Increase the number of network ports connected to the storage controller.
B. Implement a NetApp FlexGroup volume to spread the dataset across multiple constituent volumes and aggregates.
C. Use NetApp FlexCache to create a local cache of the training data on each compute node.
D. Replace the NFS protocol with iSCSI for all training data access.
E. Enable QoS maximums on the training volume to limit its IOPS.

Answer: B,C

NEW QUESTION # 27
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. 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.
C. Use SnapMirror to replicate the raw, sensitive data to Azure first, then run BlueXP classification in the cloud to find PII.
D. Manually copy raw data to Azure, run a cloud-based script to anonymize it, then start training.

Answer: B

NEW QUESTION # 28
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