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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q99-Q104):

NEW QUESTION # 99
What is the primary reason for implementing a hybrid cloud architecture in this medical imaging scenario?

A. To exclusively use cloud-native AI services, eliminating the need for any on-premises hardware.
B. To reduce the overall cost of storage by keeping all data in the public cloud.
C. 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.
D. To simplify network management by using a single, global virtual network spanning the on- premises and cloud environments.

Answer: C

NEW QUESTION # 100
The firm's CFO is concerned about the rising costs of the on-premises AI infrastructure. A storage utilization report shows that of the 200 TB of data on the high-performance AFF A-Series, 150 TB consists of inactive, older versions of product documents that are rarely accessed but must be kept online for regulatory reasons.
The current storage landscape is:
- Performance Tier: NetApp AFF A-Series (200 TB used)
- Capacity Tier: NetApp StorageGRID (1.5 PB used)
What is the most effective and automated solution to reduce the storage cost of the performance tier without impacting data accessibility?

A. Purchase an additional, larger AFF A-Series system to gain better storage efficiency through deduplication.
B. Use NetApp SnapMirror to replicate the entire 200 TB volume to the StorageGRID system and then delete the source.
C. Implement NetApp FabricPool to automatically tier the inactive data blocks from the AFF A-Series to the StorageGRID system.
D. Manually identify and delete the 150 TB of inactive data from the AFF A-Series.

Answer: C

NEW QUESTION # 101
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. Retrieval-Augmented Generation (RAG), which fetches relevant information from the documents at query time to inform the model's response.
B. Unsupervised learning, which clusters the documents into topics for the model to browse.
C. Fine-tuning, which retrains the model's last few layers on the new documents.
D. Predictive AI, which analyzes the documents to forecast future questions.

Answer: A

NEW QUESTION # 102
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 'topologyKey' is invalid; it should be 'failure-domain.beta.kubernetes.io/zone'.
B. 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'.
C. The 'vector-db-pod-0' is requesting more GPU resources than are available on any node.
D. The PersistentVolumeClaim for the pod is in a 'Pending' state.

Answer: B

NEW QUESTION # 103
The data science team in Azure reports that training jobs are taking longer than expected. An analysis of the Cloud Volumes ONTAP instance in Azure shows that the instance type is undersized for the I/O demands of the training workload. The architect needs to change the Azure VM instance type for the Cloud Volumes ONTAP system to a more powerful one.
The current configuration is:
Cloud_Provider: Azure
ONTAP_System: Cloud Volumes ONTAP (Single Node)
Current_Instance_Type: Standard_DS3_v2
Target_Instance_Type: Standard_E8s_v4
What is the most direct method to perform this operation using NetApp's management tools?

A. From the BlueXP Canvas, select the Cloud Volumes ONTAP working environment and use the
"Change Instance Type" action in the Features panel.
B. SSH to the Cloud Volumes ONTAP instance and run an ONTAP command to modify the underlying VM.
C. Terminate the existing Cloud Volumes ONTAP instance and deploy a new one with the target instance type, then restore data from backup.
D. Use the Azure portal to manually change the VM instance type.

Answer: A

NEW QUESTION # 104
......

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