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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. Cloud 3. Edge 4. On-premises - Algorithm Types 1. Reinforcement learning 2. Supervised learning 3. Unsupervised learning - AI Industry Applications 1. Digital twins 2. Healthcare applications 3. Agents - 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 - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads
Topic 2: 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. OVX architectures 2. BasePod 3. SuperPOD
Topic 3: AI Common Challenges	22%	- Resource Management 1. Sizing storage and compute resources effectively 2. Controlling costs and securing storage - Traceability and Optimization 1. Ensuring traceability for code, data, and models 2. Optimizing data access and movement 3. Maximizing performance in demanding AI workloads
Topic 4: AI Software Architectures	18%	- 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 - Scaling and Orchestration 1. Scaling AI workloads with Kubernetes 2. Leveraging BlueXP software tools
Topic 5: AI Lifecycle	27%	- 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) - Data Preparation 1. NetApp BlueXP Classification 2. XCP and CopySync 3. Data aggregation and cleansing - Model Development 1. Fine-tuning workflows 2. Inferencing 3. Model building - Generative AI Concepts 1. Hallucinations 2. Retrieval Augmented Generation (RAG) 3. Fine-tuning

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q42-Q47):

NEW QUESTION # 42
An architect is designing a fully automated, end-to-end MLOps pipeline on Kubernetes for a computer vision use case. The pipeline must handle everything from data versioning to model deployment.
The required pipeline stages are:
1. Data Versioning: Create a new, immutable version of the master dataset for the pipeline run.
2. Data Preparation: Launch a pod to run a preprocessing script on the versioned data.
3. Model Training: Launch a distributed training job that reads the prepared data from a highperformance volume.
4. Model Deployment: Push the trained model to a production inference service.
Which combination of NetApp and Kubernetes technologies provides the most effective and automated solution for this entire pipeline?

A. Use NetApp SnapMirror for data versioning and manually create hostPath volumes for each pipeline stage.
B. Use NetApp XCP to copy the data for each stage and configure static PersistentVolumes for each pod.
C. Use a single, large ReadWriteMany PVC for all stages to simplify the pipeline configuration.
D. Use the NetApp DataOps Toolkit to create a Snapshot of the source data volume (for versioning), then create a FlexClone PVC from the snapshot for the preparation stage, and finally create a FlexGroup PVC for the training stage.
E. Manually create a NetApp Snapshot via System Manager before each pipeline run, and use the NetApp DataOps Toolkit only for the training stage.

Answer: D

NEW QUESTION # 43
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. The S3 API cannot be used to access data stored on an NFS file system.
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. A NAS filer cannot be deployed on-premises.

Answer: C

NEW QUESTION # 44
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 prioritizes CPU performance over GPU performance for traditional machine learning algorithms.
B. It is designed for small-scale, departmental AI projects and cannot be scaled.
C. It is a reference architecture that is pre-validated by NetApp and NVIDIA to eliminate design complexity and ensure predictable performance for AI workloads.
D. It exclusively uses object storage, which simplifies access for data scientists using S3-native tools.

Answer: C

NEW QUESTION # 45
A data science team works primarily at a central data center but needs to run a short-term, burst- compute training job in the public cloud to take advantage of specialized GPUs that are not available on- premises. They need to efficiently and securely move a 20 TB dataset from their on- premises ONTAP cluster to a Cloud Volumes ONTAP instance for the duration of the job.
The data flow requirement is as follows:
Source: On-premises ONTAP cluster
Destination: Cloud Volumes ONTAP in AWS
Requirement: Efficient, secure, block-level data transfer.
Which NetApp technology is the most appropriate tool for this task?

A. NetApp FabricPool
B. NetApp XCP
C. NetApp SnapMirror
D. NetApp FlexCache

Answer: C

A. Prioritize fine-tuning to embed the company's brand voice and persona into the model.
B. Use RAG exclusively, as prompt engineering alone can fully replicate a specific brand voice and persona.
C. Recommend training a new LLM from scratch to ensure both brand voice and factual accuracy are built-in.
D. Propose a hybrid approach where a base model is first lightly fine-tuned for persona, then used within a RAG system for factual grounding.
E. Prioritize a RAG architecture to meet the critical requirements for factual accuracy and data traceability.

Answer: D,E

NEW QUESTION # 47
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