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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q63-Q68):

NEW QUESTION # 63
Which of the following applications use AI in the healthcare industry? (Choose two)

A. Predicting patient outcomes
B. Diagnosing diseases
C. Managing hospital supply chains
D. Automating financial reporting

Answer: A,B

NEW QUESTION # 64
The pod running the vector database on the Kubernetes cluster fails to start. An MLOps engineer runs 'kubectl describe pod vector-db-pod-0' and sees the following event message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 30s default-scheduler 0/8 nodes are available: 8 node(s) did not match pod anti-affinity rules.
The pod's manifest contains the following 'affinity' definition:
affinity:
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- labelSelector:
matchExpressions:
- key: app
operator: In
values:
- vector-db
topologyKey: "kubernetes.io/hostname"
What is the most likely reason the pod cannot be scheduled?

A. The pod's anti-affinity rule prevents it from being scheduled on any node that is already running another pod with the label 'app=vector-db'.
B. The 'vector-db-pod-0' is requesting more GPU resources than are available on any node.
C. The PersistentVolumeClaim for the pod is in a 'Pending' state.
D. The 'topologyKey' is invalid; it should be 'failure-domain.beta.kubernetes.io/zone'.

Answer: A

NEW QUESTION # 65
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 networking protocols designed to accelerate data transfer between GPUs.
B. They are specialized storage systems designed to hold large datasets for training.
C. They are integrated development environments (IDEs) used exclusively for writing Python code.
D. 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.

Answer: D

NEW QUESTION # 66
An AI architect is designing a storage solution for a new training cluster. The primary workload consists of training large language models, which involves sequential reads of massive datasets.
The key requirement is to maximize GPU utilization by providing the highest possible data throughput. Cost is a secondary concern to performance.
Which NetApp storage system is the most appropriate choice for this workload?

A. NetApp ASA (All-SAN Array)
B. NetApp C-Series
C. NetApp StorageGRID
D. NetApp E-Series

Answer: A

NEW QUESTION # 67
What is the primary reason for implementing a hybrid cloud architecture in this medical imaging scenario?

A. To reduce the overall cost of storage by keeping all data in the public cloud.
B. To leverage the powerful, elastic GPU compute resources available in the public cloud for model training while keeping sensitive source data on-premises to meet security and HIPAA compliance requirements.
C. To exclusively use cloud-native AI services, eliminating the need for any on-premises hardware.
D. To simplify network management by using a single, global virtual network spanning the on- premises and cloud environments.

Answer: B

NEW QUESTION # 68
......

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