Network Appliance NS0-901 QUESTIONS: A TERRIFIC EXAM PREPARATION SOURCE [2026]

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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Topic 1: 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
Topic 2: Industry Use Cases	- AI applications across industries 1. Healthcare AI 2. Digital twins 3. Autonomous systems and agents
Topic 3: AI Infrastructure and NetApp Solutions	- Converged workloads 1. Shared infrastructure design considerations 2. AI, HPC, and analytics convergence - AI-ready data infrastructure 1. High-performance storage for AI workloads 2. Data pipeline optimization for AI/ML workloads
Topic 4: 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. Data governance and management 2. Scalability and performance optimization

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q95-Q100):

NEW QUESTION # 95
Which of the following applications use AI in the healthcare industry? (Choose two)

A. Automating financial reporting
B. Managing hospital supply chains
C. Diagnosing diseases
D. Predicting patient outcomes

Answer: C,D

NEW QUESTION # 96
A research institute is designing an infrastructure to support its entire AI drug discovery pipeline.
The pipeline has two distinct workload requirements:
1. Training: A team of data scientists needs to train several large transformer models simultaneously using a 500 TB dataset of genomic sequences. This process requires maximum data throughput to keep the GPUs saturated.
2. Inference: Once trained, the models are deployed to an internal web portal where researchers submit individual protein sequences for analysis. These queries must return results with the lowest possible latency.
Which infrastructure design best satisfies both requirements? (Choose 2.)

A. Configure QoS minimums on the training volumes to ensure they do not impact inference performance.
B. Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.
C. Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.
D. Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.
E. Use a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.

Answer: B,C

NEW QUESTION # 97
Due to the success of the "Advisor Assistant," the number of concurrent users is expected to double in the next quarter. The existing Kubernetes cluster is running at 80% of its GPU capacity during peak hours. The architect must propose a plan to scale the compute infrastructure to handle the increased load.
Which two strategies represent the most effective and scalable solutions? (Choose 2.)

A. Manually deploy all new application replicas to a single, new, very large "super-node".
B. Vertically scale the existing nodes by adding more RAM and CPU cores to each one.
C. Replace all existing GPUs with the next-generation model to increase the performance of each node.
D. Horizontally scale the cluster by adding new GPU-equipped nodes.
E. Configure the Kubernetes Cluster Autoscaler to automatically add new nodes to the cluster from a predefined node pool when GPU demand exceeds current capacity.

Answer: D,E

NEW QUESTION # 98
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. Manually copy raw data to Azure, run a cloud-based script to anonymize it, then start training.
B. Use FabricPool to automatically tier all raw data to Azure Blob storage, then mount the blob container to the training VMs.
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 # 99
An AI research team is experiencing slow model training times. Their performance monitoring indicates that the GPUs are frequently idle, waiting for data. They want to implement a single technology change to create a more direct data path between their storage and GPUs.
Their current setup is as follows:
Compute: Server with NVIDIA A100 GPUs
Storage: NetApp AFF A-Series (All-Flash)
Network: 100GbE Ethernet
Data_Path: Storage -> Host CPU/Memory -> GPU Memory
Which technology should the architect recommend to specifically address this data path inefficiency?

A. GPUDirect Storage
B. A faster CPU in the server
C. NetApp SnapMirror
D. NetApp FabricPool

Answer: A

NEW QUESTION # 100
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