NS0-901 Prüfungsfragen, NS0-901 PDF Demo

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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Cloud and Hybrid Cloud AI Deployment	18%	- NetApp cloud data services for AI - Cloud-native AI solutions and integration - Data mobility and consistency across environments - Hybrid and multi-cloud AI architectures
NetApp AI Solutions and Architecture	25%	- Storage architectures for AI workloads - Data management and data pipeline design - ONTAP integration with AI frameworks - Scalability and performance optimization for AI - NetApp AI-ready infrastructure components
AI Lifecycle	27%	- Model training, inference, and optimization - AI governance, ethics, and compliance - Data preparation and management for AI - AI lifecycle stages: design, training, deployment, monitoring - Predictive vs generative AI
Security, Reliability, and Operations	15%	- Monitoring, logging, and troubleshooting AI environments - Cost management and efficiency - Data security and access control for AI - High availability and data protection
AI Overview	15%	- AI deployment models: on-premises, cloud, edge - Algorithm types: supervised, unsupervised, reinforcement learning - AI, machine learning, and deep learning concepts - Convergence of AI, high-performance computing, and analytics - AI industry use cases and applications

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Network Appliance NetApp Certified AI Expert Exam NS0-901 Prüfungsfragen mit Lösungen (Q51-Q56):

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

Antwort: A

52. Frage
Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)

A. RunAI
B. TensorFlow Extended
C. Google VertexAI
D. KubeFlow

Antwort: A,D

53. Frage
An organization is developing a new AI-powered application. The initial phase involves feeding a curated 50 TB dataset of labeled images into a complex neural network, allowing the model to learn and adjust its internal parameters over millions of iterations. The second phase involves deploying this finalized model to a web service where it will process single, user-uploaded images and return a classification in real-time.
Which statement accurately describes these two phases?

A. Phase 1 is inferencing, and Phase 2 is training.
B. Both Phase 1 and Phase 2 are examples of inferencing.
C. Both Phase 1 and Phase 2 are examples of training.
D. Phase 1 is training, and Phase 2 is inferencing.

Antwort: D

54. Frage
An architect is designing a scalable, automated MLOps platform using Kubeflow on a Kubernetes cluster. The platform must support the entire AI lifecycle for multiple teams, with different storage requirements at each stage.
The key requirements are:
- Data Ingestion: A pipeline step needs a shared, read-write volume accessible by multiple pods to stage raw data.
- Experimentation: Data scientists need individual, isolated volumes for their Jupyter notebooks.
- Training: Distributed training jobs require a high-performance, parallel-access filesystem for reading training data.
- Automation: All storage must be provisioned automatically via Kubeflow pipeline definitions without manual intervention.
Which combination of technologies and configurations would create the most effective solution?

A. Create a single, large NFS volume and mount it to all pods using a static PersistentVolume.
B. Rely on hostPath volumes for all storage to ensure the highest performance.
C. Configure multiple Trident backends (e.g., 'ontap-nas' for standard volumes, 'ontap-nas-flexgroup' for parallel access) and corresponding StorageClasses.
D. Use the NetApp DataOps Toolkit for Python within the Kubeflow pipeline components to dynamically create and manage Trident PVCs for each stage.
E. Use the NetApp DataOps Toolkit for all storage provisioning, bypassing Trident and Kubernetes PVCs.

Antwort: C,D

55. Frage
An organization has a core data center with a large AI training cluster and several remote edge locations for data ingest and local inference. The edge locations frequently need access to the latest models trained in the core data center, but WAN bandwidth is limited and can be unreliable.
Users at the edge are reporting slow model loading times.
An architect reviews the data access logs from an edge site:
Timestamp: 2025-07-11T15:30:00Z
Event: Model_Load_Request
Model_Path: nfs://core-filer.example.com/vol/models/latest_model.pkl
Source_IP: 192.168.100.15 (Edge Server)
Destination_IP: 10.1.1.50 (Core Filer)
Status: SUCCESS
Duration: 3600s (60 minutes)
What is the most likely cause of the slow model loading times at the edge?

A. The edge server does not have enough RAM to cache the model effectively.
B. The model file is being transferred over a slow, high-latency WAN link for every load request.
C. The core ONTAP filer is using slow, capacity-based disks.
D. The NFS version used between the core and edge is outdated.

Antwort: B

56. Frage
......

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