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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Common Challenges	22%	- Resource Management 1. Controlling costs and securing storage 2. Sizing storage and compute resources effectively - Traceability and Optimization 1. Maximizing performance in demanding AI workloads 2. Ensuring traceability for code, data, and models 3. Optimizing data access and movement
AI Overview	15%	- AI Industry Applications 1. Agents 2. Digital twins 3. Healthcare applications - Algorithm Types 1. Reinforcement learning 2. Supervised learning 3. Unsupervised learning - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - AI Deployment Models 1. Cloud 2. Edge 3. Benefits and risks of each model 4. On-premises - Machine Learning Fundamentals 1. Understand the relationship between AI, machine learning, and deep learning 2. Describe machine learning benefits - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics
AI Hardware Architectures	18%	- NetApp Architectures 1. OVX architectures 2. SuperPOD 3. BasePod - Infrastructure Topologies 1. Data aggregation and compute topologies - Networking and Storage 1. Storage architectures for AI 2. Network protocols for AI workloads
AI Lifecycle	27%	- Model Development 1. Model building 2. Inferencing 3. Fine-tuning workflows - Generative AI Concepts 1. Fine-tuning 2. Retrieval Augmented Generation (RAG) 3. Hallucinations - Predictive AI vs. Generative AI 1. Impact of generative content (text, images, video, decision-making) 2. Large Language Models (LLMs) 3. Distinction between predictive and generative AI - Data Preparation 1. Data aggregation and cleansing 2. XCP and CopySync 3. NetApp BlueXP Classification
AI Software Architectures	18%	- Development Tools 1. Jupyter notebooks vs. pipelines 2. NetApp DataOps Toolkit - MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale - Scaling and Orchestration 1. Leveraging BlueXP software tools 2. Scaling AI workloads with Kubernetes

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2026 Realistic Network Appliance Exam NS0-901 Question

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q51-Q56):

NEW QUESTION # 51
A data scientist on the team wants to run an experiment on a new vector indexing strategy. To do this, they need a temporary, writable copy of the main vector database. They use the NetApp DataOps Toolkit for Python to automate this process.
The following script is executed:
from netapp_dataops.k8s import clone_pvc
clone_pvc(
source_pvc_name="prod-vector-db-pvc",
new_pvc_name="exp-indexing-clone",
namespace="ds-prod"
)
Assuming the source PVC exists and the user has correct permissions, what is the primary benefit of using this method?

A. It automatically migrates the data from the AFF A-Series to the StorageGRID system for the experiment.
B. It creates a full physical copy of the data, providing the best performance isolation.
C. It uses NetApp FlexClone technology to create a near-instantaneous, space-efficient writable copy of the volume, accelerating the experimental workflow.
D. It creates a read-only Snapshot, ensuring the integrity of the experimental data.

Answer: C

NEW QUESTION # 52
To meet HIPAA compliance, the first step in the data pipeline is to identify all medical scans that contain embedded PII. The solution must be automated and capable of scanning data in-place on the on-premises ASA system.
Which two technologies should be used to accomplish this identification and tagging task?
(Choose 2.)

A. A custom "PII" category within BlueXP classification to identify specific medical record number formats.
B. NetApp BlueXP classification, configured to scan the on-premises ASA working environment.
C. A custom Python script that uses regular expressions to search file contents.
D. A manual review process where technicians visually inspect each scan for PII.
E. NetApp SnapLock to make the source data immutable before scanning.

Answer: A,B

NEW QUESTION # 53
An AI infrastructure engineer is troubleshooting a poorly performing distributed training job. The job is running across multiple nodes, each equipped with powerful GPUs. The engineer observes that overall GPU utilization is unexpectedly low. System-level monitoring on the compute nodes provides the following metrics during a training run.
avg_gpu_utilization: 25%
avg_cpu_iowait_percent: 65%
avg_network_bandwidth_util: 95% (on a 10GbE network)
storage_array_latency: <1ms
Given these metrics, what is the most likely bottleneck causing the low GPU utilization?

A. The network connecting the compute nodes and storage is saturated and has become the primary bottleneck.
B. The GPUs are faulty and cannot process data at their rated speed.
C. The storage array is too slow and cannot serve data quickly enough.
D. The CPU is underpowered and cannot preprocess the data fast enough for the GPUs.

Answer: A

NEW QUESTION # 54
The firm has acquired a competitor, and the volume of proprietary documents for the "Advisor Assistant" is expected to triple, exceeding the capacity of the current StorageGRID data lake. The architect needs to expand the data lake's capacity non-disruptively.
The current StorageGRID status is:
System_Health: Nominal
Node_Count: 6 (3 Storage Nodes, 3 Admin/Gateway Nodes)
Usable_Capacity: 1.5 PB
Used_Capacity: 1.4 PB (93%)
What is the standard procedure for scaling the capacity of the on-premises NetApp StorageGRID system?

A. Provision a new, separate StorageGRID system and use BlueXP copy and sync to move the data.
B. Use FabricPool to tier the excess data from StorageGRID to a public cloud provider.
C. Add new storage nodes to the existing StorageGRID system and execute an expansion procedure.
D. Replace the disks in the existing storage nodes with higher-capacity drives.

Answer: C

NEW QUESTION # 55
An AI training job is running slower than expected. The infrastructure team suspects a compute bottleneck. The job involves processing high-resolution images with a complex convolutional neural network (CNN). They review the logs from the training script.
Epoch 1/100 - 3600s - loss: 1.253 - acc: 0.54
...
CPU_Utilization_During_Epoch: 99% (all cores)
GPU_0_Utilization_During_Epoch: 8%
GPU_1_Utilization_During_Epoch: 7%
...
Epoch 2/100 - 3610s - loss: 1.102 - acc: 0.61
What is the most likely cause of the poor training performance?

A. The storage system cannot deliver data fast enough to the compute node.
B. The network is saturated, preventing the GPUs from receiving data.
C. The model is too simple and does not effectively utilize the GPU's parallel processing capabilities.
D. The training script is CPU-bound, likely performing data augmentation or preprocessing on the CPU instead of offloading it to the GPUs.

Answer: D

NEW QUESTION # 56
......

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