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Network Appliance NS0-901 Exam Syllabus Topics:

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

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

NEW QUESTION # 95
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 storage array is too slow and cannot serve data quickly enough.
B. The network connecting the compute nodes and storage is saturated and has become the primary bottleneck.
C. The GPUs are faulty and cannot process data at their rated speed.
D. The CPU is underpowered and cannot preprocess the data fast enough for the GPUs.

Answer: B

NEW QUESTION # 96
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. Add new storage nodes to the existing StorageGRID system and execute an expansion procedure.
C. Use FabricPool to tier the excess data from StorageGRID to a public cloud provider.
D. Replace the disks in the existing storage nodes with higher-capacity drives.

Answer: B

NEW QUESTION # 97
What is the primary role of NetApp Trident in a Kubernetes environment designed for AI workloads?

A. To directly accelerate GPU computations using specialized drivers.
B. To act as a dynamic storage orchestrator, provisioning persistent storage from NetApp backends on- demand for containerized applications.
C. To function as a container runtime interface for executing AI models.
D. To provide a web-based IDE, like Jupyter, for data scientists to develop models.

Answer: B

NEW QUESTION # 98
A robotics company is developing a control system for an autonomous warehouse drone. The drone must learn to navigate complex environments to pick up packages. The development team has created a physics-based simulation where the drone can attempt the task millions of times.
The drone receives a positive reward for successfully retrieving a package and a negative penalty for collisions. Which type of machine learning algorithm is being used in this scenario?

A. Supervised learning
B. Generative learning
C. Reinforcement learning
D. Unsupervised learning

Answer: C

NEW QUESTION # 99
Which of the following platforms provides tools for model training and deployment specifically for AI workloads?

A. Google VertexAI
B. Domino Data Labs
C. All of the above
D. RunAI

Answer: C

NEW QUESTION # 100
......

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