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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
AI Infrastructure and NetApp Solutions	- AI-ready data infrastructure 1. Data pipeline optimization for AI/ML workloads 2. High-performance storage for AI workloads - Converged workloads 1. AI, HPC, and analytics convergence 2. Shared infrastructure design considerations
Industry Use Cases	- AI applications across industries 1. Autonomous systems and agents 2. Healthcare AI 3. Digital twins
AI Lifecycle and Deployment	- Operational challenges 1. Data governance and management 2. Scalability and performance optimization - End-to-end AI lifecycle 1. Model training and validation 2. Data ingestion and preparation 3. Model deployment and monitoring
AI and Machine Learning Fundamentals	- AI, ML, DL concepts 1. Differences between AI, machine learning, and deep learning 2. Training, inference, and prediction workflows - Algorithm types 1. Unsupervised learning 2. Reinforcement learning 3. Supervised learning

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q28-Q33):

NEW QUESTION # 28
The firm has acquired a competitor, and the volume of proprietary documents for the "Advisor Assistant" is expected to triple, exceeding the capacity of the current StorageGRID data lake. The architect needs to expand the data lake's capacity non-disruptively.
The current StorageGRID status is:
System_Health: Nominal
Node_Count: 6 (3 Storage Nodes, 3 Admin/Gateway Nodes)
Usable_Capacity: 1.5 PB
Used_Capacity: 1.4 PB (93%)
What is the standard procedure for scaling the capacity of the on-premises NetApp StorageGRID system?

A. Add new storage nodes to the existing StorageGRID system and execute an expansion procedure.
B. Use FabricPool to tier the excess data from StorageGRID to a public cloud provider.
C. Provision a new, separate StorageGRID system and use BlueXP copy and sync to move the data.
D. Replace the disks in the existing storage nodes with higher-capacity drives.

Answer: A

NEW QUESTION # 29
A university is building a shared AI research platform. They have two primary requirements:
1. Performance: A "hot" research area for active model training and development that requires the absolute lowest latency and highest throughput to support multiple, simultaneous GPU- intensive jobs.
The data in this area is around 50 TB.
2. Capacity & Cost: A "cold" data lake to store over 5 PB of raw, unstructured experimental data that is infrequently accessed but must be retained for compliance and future use. This tier must be as costeffective as possible.
Which combination of NetApp hardware and technologies should an architect select to build a complete, optimized, and cost-effective solution? (Select all that apply.)

A. Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
B. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.
C. Use a standard 10GbE network for all connectivity to reduce costs.
D. Use NetApp StorageGRID to build the 5 PB cost-effective data lake.
E. Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
F. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.

Answer: A,D,E,F

NEW QUESTION # 30
A pharmaceutical company is creating a "digital twin" of its manufacturing process. This involves running complex simulations (an HPC workload) that generate massive datasets.
The company wants to use this data immediately for two other purposes:
1. Analytics: Business analysts need to run complex queries on the simulation output using tools like Spark.
2. AI Training: Data scientists need to use the same output as a training set for a predictive maintenance model.
The company wants to avoid creating separate data silos for each workload.
Which two NetApp technologies are best suited for building a unified data lake that can efficiently serve all three workloads (HPC, Analytics, AI)? (Choose 2.)

A. NetApp Autonomous Ransomware Protection to secure the data from modification.
B. NetApp ONTAP with FlexGroup volumes to provide high-throughput, parallel NFS access for the HPC and AI training workloads.
C. NetApp Keystone to provide a flexible, pay-as-you-go consumption model for the infrastructure.
D. NetApp StorageGRID to provide a scalable, S3-native object store that integrates directly with modern analytics platforms like Spark.
E. NetApp SnapCenter to create application-consistent backups of the data.

Answer: B,D

NEW QUESTION # 31
A data science team reports that their Jupyter notebook pod, which was previously working, is now failing to start. The pod's status is 'CrashLoopBackOff'. An MLOps engineer investigates and finds that the pod's PersistentVolumeClaim (PVC) is bound, but the pod logs show a "Permission denied" error when trying to write to its '/data' mount point.
The engineer checks the Trident backend configuration associated with the pod's StorageClass:
apiVersion: trident.netapp.io/v1
kind: TridentBackendConfig
metadata:
name: ontap-nas-eco
spec:
version: 1
storageDriverName: ontap-nas
managementLIF: 10.10.20.5
dataLIF: 10.10.20.10
svm: svm-prod-ds
exportPolicy: read-only-policy
What is the most likely cause of the "Permission denied" error?

A. The Kubernetes pod has an invalid 'securityContext' that prevents it from writing to any volume.
B. The 'storageDriverName' should be 'ontap-san' for all AI workloads.
C. The Trident backend is configured to use an export policy ('read-only-policy') that does not grant write permissions to the Kubernetes nodes.
D. The 'dataLIF' is configured incorrectly and is unreachable from the Kubernetes nodes.

Answer: C

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

Answer: B,E

NEW QUESTION # 33
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