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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Hardware Architectures	18%	- NetApp Architectures 1. SuperPOD 2. OVX architectures 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 Overview	15%	- AI Deployment Models 1. Benefits and risks of each model 2. On-premises 3. Cloud 4. Edge - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - Machine Learning Fundamentals 1. Understand the relationship between AI, machine learning, and deep learning 2. Describe machine learning benefits - AI Industry Applications 1. Healthcare applications 2. Agents 3. Digital twins - Algorithm Types 1. Reinforcement learning 2. Supervised learning 3. Unsupervised learning
AI Common Challenges	22%	- Resource Management 1. Sizing storage and compute resources effectively 2. Controlling costs and securing storage - 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 Lifecycle	27%	- Model Development 1. Inferencing 2. Model building 3. Fine-tuning workflows - Data Preparation 1. XCP and CopySync 2. Data aggregation and cleansing 3. NetApp BlueXP Classification - Predictive AI vs. Generative AI 1. Large Language Models (LLMs) 2. Distinction between predictive and generative AI 3. Impact of generative content (text, images, video, decision-making) - Generative AI Concepts 1. Hallucinations 2. Fine-tuning 3. Retrieval Augmented Generation (RAG)
AI Software Architectures	18%	- Development Tools 1. NetApp DataOps Toolkit 2. Jupyter notebooks vs. pipelines - 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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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q70-Q75):

NEW QUESTION # 70
An organization is planning to deploy a large AI infrastructure but wants to avoid a large, upfront capital expenditure. They prefer an operational expenditure (OpEx) model where they pay for storage and compute resources as they are consumed. They also need the flexibility to scale resources up or down based on project demands.
Which NetApp consumption model is specifically designed to meet these financial and operational requirements?

A. NetApp Keystone, which provides a subscription-based, pay-as-you-go service for on-premises and cloud storage.
B. A perpetual licensing model for all ONTAP software features.
C. NetApp Cloud Volumes ONTAP PAYGO licensing only.
D. A standard capital purchase of AFF and ASA systems.

Answer: A

A. Both projects should use NetApp StorageGRID as the primary storage for training to ensure low latency.
B. Project A requires only a large amount of data, while Project B requires only a large amount of code.
C. Both projects are examples of fine-tuning and have similar infrastructure needs.
D. Project B is a fine-tuning task, which leverages an existing model and requires less data and compute resources than building a model from scratch.
E. Project A is a model building task and requires a significantly larger and more powerful compute and storage infrastructure than Project B.

Answer: D,E

NEW QUESTION # 72
A research lab uses a fleet of autonomous drones to collect high-resolution aerial imagery for agricultural analysis. The drones land at a remote edge location and offload their data. The AI models for image analysis are trained at a central data center. The team is using NetApp SnapMirror to replicate the data from the edge to the core. However, the data scientists are complaining that the datasets arriving at the data center are often incomplete or corrupted.
An administrator reviews the SnapMirror configuration and status via the BlueXP API:
{
"source": { "workingEnvironmentId": "OnPrem-Edge-Filer-1", "volumeName": "drone_data_raw" },
"destination": { "workingEnvironmentId": "Core-Datacenter-A800", "volumeName":
"drone_data_replicated" },
"mirrorState": "broken-off",
"relationshipStatus": "idle",
"unhealthyReason": "Transfer failed. Destination volume is out of space.", "lastTransferInfo": {
"transferError": "No space left on device"
}
}
What is the direct cause of the incomplete datasets at the data center?

A. The BlueXP Connector does not have the correct permissions to manage the SnapMirror relationship.
B. The source volume at the edge location has become corrupted.
C. The network connection between the edge and the core is unreliable.
D. The SnapMirror relationship is broken because the destination volume at the core data center has run out of capacity.

Answer: D

NEW QUESTION # 73
A university is building a shared AI research platform. They have two primary requirements:
1. Performance: A "hot" research area for active model training and development that requires the absolute lowest latency and highest throughput to support multiple, simultaneous GPU- intensive jobs.
The data in this area is around 50 TB.
2. Capacity & Cost: A "cold" data lake to store over 5 PB of raw, unstructured experimental data that is infrequently accessed but must be retained for compliance and future use. This tier must be as costeffective as possible.
Which combination of NetApp hardware and technologies should an architect select to build a complete, optimized, and cost-effective solution? (Select all that apply.)

A. Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
B. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.
C. Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
D. Use NetApp StorageGRID to build the 5 PB cost-effective data lake.
E. Use a standard 10GbE network for all connectivity to reduce costs.
F. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.

Answer: A,B,C,D

NEW QUESTION # 74
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 training script is CPU-bound, likely performing data augmentation or preprocessing on the CPU instead of offloading it to the GPUs.
C. The network is saturated, preventing the GPUs from receiving data.
D. The model is too simple and does not effectively utilize the GPU's parallel processing capabilities.

Answer: B

NEW QUESTION # 75
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