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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. SuperPOD 2. BasePod 3. OVX architectures
Topic 2: AI Lifecycle	27%	- Model Development 1. Model building 2. Inferencing 3. Fine-tuning workflows - 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) - Generative AI Concepts 1. Hallucinations 2. Fine-tuning 3. Retrieval Augmented Generation (RAG) - Data Preparation 1. NetApp BlueXP Classification 2. XCP and CopySync 3. Data aggregation and cleansing
Topic 3: AI Software Architectures	18%	- MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale - Development Tools 1. NetApp DataOps Toolkit 2. Jupyter notebooks vs. pipelines - Scaling and Orchestration 1. Scaling AI workloads with Kubernetes 2. Leveraging BlueXP software tools
Topic 4: AI Common Challenges	22%	- Traceability and Optimization 1. Ensuring traceability for code, data, and models 2. Optimizing data access and movement 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 Overview	15%	- Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - AI Industry Applications 1. Healthcare applications 2. Digital twins 3. Agents - Algorithm Types 1. Reinforcement learning 2. Unsupervised learning 3. Supervised learning - AI Deployment Models 1. Cloud 2. Edge 3. Benefits and risks of each model 4. On-premises - Machine Learning Fundamentals 1. Understand the relationship between AI, machine learning, and deep learning 2. Describe machine learning benefits

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q79-Q84):

NEW QUESTION # 79
An AI architect is planning the resource allocation for a new project. The primary task is to process millions of unlabeled customer reviews to identify naturally occurring groups or themes without any prior guidance.
The project requirements are summarized below:
Task: Discover hidden patterns in text data
Input_Data: 10 million unlabeled text reviews
Output: Clustered groups of related reviews
Supervision: None
Which type of machine learning algorithm is required for this task?

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

Answer: B

NEW QUESTION # 80
An MLOps team uses a variety of platforms to manage their AI workloads. They need to understand the primary function of each tool within their ecosystem. Which statement best describes the role of an MLOps/LLMOps platform like Kubeflow or Run:AI?

A. They are specialized storage systems designed to hold large datasets for training.
B. They are integrated development environments (IDEs) used exclusively for writing Python code.
C. They are orchestration and management platforms that automate and streamline the entire AI/ML lifecycle, from data preparation and model training to deployment and monitoring.
D. They are networking protocols designed to accelerate data transfer between GPUs.

Answer: C

NEW QUESTION # 81
What is the primary difference between C-Series and A-Series storage for AI workloads?

A. A-Series is for smaller-scale AI workloads, while C-Series supports large-scale, high-performance AI applications.
B. A-Series provides GPU-based acceleration, while C-Series only supports CPU-based workloads.
C. C-Series supports both file and object-based storage, while A-Series is optimized for object storage.
D. C-Series is designed for high-performance workloads, while A-Series focuses on cost-efficiency for general storage.

Answer: D

NEW QUESTION # 82
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 FlexCache
B. NetApp XCP
C. NetApp FabricPool
D. NetApp SnapMirror

Answer: D

NEW QUESTION # 83
The company decides to establish a disaster recovery (DR) site in a secondary data center for the entire Digital Twin platform. The DR plan must protect the HPC data, the AI training data, and the central data lake.
The DR requirements are:
- RPO: 4 hours for all data.
- RTO: 24 hours for the entire platform.
- Process: The failover and failback process should be as automated as possible.
Which combination of technologies provides the most comprehensive DR solution?

A. Use NetApp SnapMirror for the ASA, StorageGRID replication for the data lake, and rely on tape backups for the E-Series/BeeGFS data.
B. Use NetApp SnapMirror for the ASA, StorageGRID's built-in replication for the data lake, and a host- based tool like 'rsync' for the E-Series/BeeGFS data, all orchestrated via BlueXP disaster recovery.
C. Deploy a MetroCluster configuration spanning the primary and DR sites for all three storage systems.
D. Use BlueXP backup and recovery to back up all three storage systems (ASA, E-Series, StorageGRID) to a single cloud target.

Answer: B

NEW QUESTION # 84
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