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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Overview	15%	- AI Deployment Models 1. Benefits and risks of each model 2. Edge 3. On-premises 4. Cloud - AI Industry Applications 1. Digital twins 2. Agents 3. Healthcare applications - 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 - Algorithm Types 1. Unsupervised learning 2. Supervised learning 3. Reinforcement learning - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads
Topic 2: AI Lifecycle	27%	- Generative AI Concepts 1. Hallucinations 2. Retrieval Augmented Generation (RAG) 3. Fine-tuning - Model Development 1. Fine-tuning workflows 2. Inferencing 3. Model building - Data Preparation 1. XCP and CopySync 2. Data aggregation and cleansing 3. NetApp BlueXP Classification - Predictive AI vs. Generative AI 1. Impact of generative content (text, images, video, decision-making) 2. Distinction between predictive and generative AI 3. Large Language Models (LLMs)
Topic 3: AI Software Architectures	18%	- Scaling and Orchestration 1. Leveraging BlueXP software tools 2. Scaling AI workloads with Kubernetes - 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
Topic 4: AI Common Challenges	22%	- Traceability and Optimization 1. Optimizing data access and movement 2. Ensuring traceability for code, data, and models 3. Maximizing performance in demanding AI workloads - Resource Management 1. Controlling costs and securing storage 2. Sizing storage and compute resources effectively
Topic 5: AI Hardware Architectures	18%	- Infrastructure Topologies 1. Data aggregation and compute topologies - NetApp Architectures 1. SuperPOD 2. BasePod 3. OVX architectures - Networking and Storage 1. Storage architectures for AI 2. Network protocols for AI workloads

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q47-Q52):

NEW QUESTION # 47
Which AI technology is used to generate new, never-before-seen content such as images or text?

A. Reinforcement AI
B. Predictive AI
C. Supervised AI
D. Generative AI

Answer: D

NEW QUESTION # 48
What is the primary architectural advantage of using a NetApp AIPod with NVIDIA DGX servers for the AI training cluster, as described in the scenario?

A. It is a reference architecture that is pre-validated by NetApp and NVIDIA to eliminate design complexity and ensure predictable performance for AI workloads.
B. It exclusively uses object storage, which simplifies access for data scientists using S3-native tools.
C. It is designed for small-scale, departmental AI projects and cannot be scaled.
D. It prioritizes CPU performance over GPU performance for traditional machine learning algorithms.

Answer: A

NEW QUESTION # 49
An AI architect is reviewing the design for a new data lake. The primary requirement is to store petabytes of unstructured data (images, video, sensor logs) in a highly durable, scalable, and cost- effective manner. The data will be accessed via S3 API by various data processing and analytics applications.
The initial design proposes using a traditional Network Attached Storage (NAS) filer with a large number of disks. The architect reviews the proposal:
Proposed_System: Traditional NAS Filer
Protocol: NFSv4
Scalability_Model: Scale-up
Metadata_Handling: Centralized in filer head
Cost_per_GB: Moderate
Why is this proposed system a poor choice for a petabyte-scale data lake?

A. NFS is incapable of storing image or video files.
B. A NAS filer cannot be deployed on-premises.
C. A traditional scale-up NAS system will face scalability and cost-effectiveness challenges at the petabyte scale compared to an object storage system.
D. The S3 API cannot be used to access data stored on an NFS file system.

Answer: C

NEW QUESTION # 50
A data scientist needs to launch a Jupyter notebook as a pod in a Kubernetes cluster. The pod requires a 50 Gi persistent volume for storing datasets and notebooks. The cluster administrator has configured a default Trident StorageClass for general-purpose use. The data scientist has the following PersistentVolumeClaim (PVC) manifest:
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: jupyter-pvc
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 50Gi
When this PVC is applied to the cluster, what will be the result?

A. Trident will automatically provision a 50 Gi volume on its default backend and bind it to the PVC.
B. The PVC will fail because a 'storageClassName' is not explicitly defined.
C. Trident will create a 1 Gi volume, as this is the default size for all PVCs.
D. The PVC will remain in a "Pending" state until a PersistentVolume is manually created.

Answer: A

NEW QUESTION # 51
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 # 52
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