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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Lifecycle	27%	- 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) - Model Development 1. Inferencing 2. Fine-tuning workflows 3. Model building - Generative AI Concepts 1. Hallucinations 2. Fine-tuning 3. Retrieval Augmented Generation (RAG) - Data Preparation 1. NetApp BlueXP Classification 2. Data aggregation and cleansing 3. XCP and CopySync
Topic 2: AI Hardware Architectures	18%	- NetApp Architectures 1. SuperPOD 2. BasePod 3. OVX architectures - Networking and Storage 1. Network protocols for AI workloads 2. Storage architectures for AI - Infrastructure Topologies 1. Data aggregation and compute topologies
Topic 3: 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. Optimizing data access and movement 3. Ensuring traceability for code, data, and models
Topic 4: AI Overview	15%	- AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - Machine Learning Fundamentals 1. Understand the relationship between AI, machine learning, and deep learning 2. Describe machine learning benefits - Algorithm Types 1. Unsupervised learning 2. Supervised learning 3. Reinforcement learning - AI Industry Applications 1. Digital twins 2. Healthcare applications 3. Agents - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - AI Deployment Models 1. On-premises 2. Cloud 3. Benefits and risks of each model 4. Edge
Topic 5: AI Software Architectures	18%	- Scaling and Orchestration 1. Scaling AI workloads with Kubernetes 2. Leveraging BlueXP software tools - MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale - Development Tools 1. Jupyter notebooks vs. pipelines 2. NetApp DataOps Toolkit

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q104-Q109):

NEW QUESTION # 104
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 remain in a "Pending" state until a PersistentVolume is manually created.
C. Trident will create a 1 Gi volume, as this is the default size for all PVCs.
D. The PVC will fail because a 'storageClassName' is not explicitly defined.

Answer: A

NEW QUESTION # 105
A development team is building a generative AI application that must answer questions based on a constantly changing internal knowledge base of company documents. They want to provide the model with up-to-date information without altering its core weights and capabilities.
Which approach is most suitable for this requirement?

A. Predictive AI, which analyzes the documents to forecast future questions.
B. Fine-tuning, which retrains the model's last few layers on the new documents.
C. Unsupervised learning, which clusters the documents into topics for the model to browse.
D. Retrieval-Augmented Generation (RAG), which fetches relevant information from the documents at query time to inform the model's response.

Answer: D

NEW QUESTION # 106
An architect is designing a comprehensive AI platform for a large enterprise. The platform must support the entire data lifecycle, from ingest at the edge to a central data lake, and finally to a high- performance training cluster.
The requirements are:
- Edge Ingest: Data must be collected at remote sites and efficiently replicated to the core.
- Data Lake: A central, petabyte-scale repository for unstructured data, accessible via the S3 protocol.
- Training Cluster: A high-performance compute cluster that requires low-latency, parallel file access to training datasets.
- Data Traceability: All datasets used for training must be immutably versioned.
Which combination of NetApp technologies and protocols should the architect choose to build this solution? (Select all that apply.)

A. Use NetApp Snapshots on the training dataset volumes to create immutable, point-in-time versions for traceability.
B. Use NetApp StorageGRID to create the petabyte-scale, S3-accessible data lake at the core.
C. Use a NetApp E-Series system with a parallel file system (like BeeGFS) to provide high- performance, parallel file access for the training cluster.
D. Use iSCSI as the primary protocol for the data lake to ensure maximum compatibility.
E. Use NetApp FlexCache to tier cold data from the data lake to the public cloud.
F. Use NetApp ONTAP systems at the edge and NetApp SnapMirror to replicate data to the core data center.

Answer: A,B,C,F

NEW QUESTION # 107
Given the firm's requirements for using a private, constantly updated knowledge base and the strict mandate for data traceability, which AI architecture is the most appropriate foundation for the "Advisor Assistant" chatbot?

A. A standalone LLM deployed in an air-gapped environment with no access to the document repository.
B. A Retrieval-Augmented Generation (RAG) architecture that retrieves relevant context from a local vector database to enrich prompts sent to the LLM.
C. A fine-tuning architecture where a base LLM is continuously retrained on the entire document repository.
D. A predictive AI model trained to forecast market trends.

Answer: B

NEW QUESTION # 108
In the data pipeline, which NetApp technology is used to create an efficient, block-level, incremental replication of the anonymized datasets from the on-premises ASA to the Cloud Volumes ONTAP instance in Azure?

A. NetApp XCP
B. NetApp BlueXP copy and sync
C. NetApp FabricPool
D. NetApp SnapMirror

Answer: D

NEW QUESTION # 109
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