NS0-901 Actual Dumps | NS0-901 Exam Tutorial

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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
The data scientists report that their Kubernetes-based data preparation jobs are failing. The pods are stuck in a 'Pending' state.
An MLOps engineer runs 'kubectl describe pvc data-prep-pvc-01' and sees the following event:
Type Reason Age From Message
- - - -
Warning ProvisioningFailed 2m15s trident-orchestrator-7b... failed to provision volume with StorageClass "bronze-tier": backend unavailable: no healthy backend with satisfying attributes for storage class "bronze-tier" The engineer checks the Trident backend configurations and finds no backend associated with the "bronze-tier" StorageClass.
What is the root cause of the failure?

A. The Kubernetes scheduler is unable to find a node with enough resources for the pods.
B. The data preparation pods do not have the correct security permissions.
C. The StorageGRID data lake is offline, preventing the creation of new volumes.
D. The PersistentVolumeClaim is referencing a StorageClass that has no configured or healthy Trident backend to provision storage from.

Answer: D

NEW QUESTION # 24
The data anonymization job, running in a Kubernetes pod, fails. The pod logs show a "Permission Denied" error when trying to access the source volume on the on-premises ASA. An administrator checks the export policy rule for the volume.
The active rule is as follows:
Rule_Index: 1
Client_Match: 10.50.0.0/16
Protocols: nfs4
Read_Only_Access: sys
Read_Write_Access: -
Superuser_Access: none
The Kubernetes pod that failed has an IP address of '10.60.5.10'.
What is the cause of the "Permission Denied" error?

A. The pod's IP address ('10.60.5.10') is not within the allowed client match range ('10.50.0.0/16').
B. The export policy only allows access via the NFSv4 protocol.
C. The 'Superuser_Access' setting is too restrictive.
D. The export policy does not grant read-write access, which is required by the anonymization job.

Answer: A

A. Both projects are examples of fine-tuning and have similar infrastructure needs.
B. Project B is a fine-tuning task, which leverages an existing model and requires less data and compute resources than building a model from scratch.
C. Project A requires only a large amount of data, while Project B requires only a large amount of code.
D. Project A is a model building task and requires a significantly larger and more powerful compute and storage infrastructure than Project B.
E. Both projects should use NetApp StorageGRID as the primary storage for training to ensure low latency.

Answer: B,D

NEW QUESTION # 26
The firm decides to expand the "Advisor Assistant" project to a new team in a different department. This team needs its own isolated environment. An MLOps engineer attempts to submit a new GPU- intensive job for the new team, but it remains pending. The engineer checks the Run:AI scheduler logs and finds the following entry:
time="2025-07-11T16:45:00Z" level=info msg="Job ds-new-team-job1 cannot be scheduled.
Project 'new-team-project' has exceeded its GPU quota. Quota: 0, Requested: 1, Used: 0." What is the root cause of the scheduling failure?

A. A Run:AI project quota has been configured for the new team, but it has been set to zero, effectively blocking them from using any GPU resources.
B. The job is requesting a specific type of GPU that is not available in the cluster.
C. The Run:AI scheduler is offline and cannot process new jobs.
D. The Kubernetes cluster has no available GPUs.

Answer: A

NEW QUESTION # 27
The company's finance department mandates a cost-control strategy for the petabyte-scale StorageGRID data lake. The analysis shows that 80% of the simulation data is not accessed after
90 days but must be retained for five years for regulatory compliance. Which StorageGRID feature should the architect use to automatically reduce the long-term storage costs for this inactive data?

A. Enable deduplication and compression on all StorageGRID storage nodes.
B. Implement a FabricPool policy to tier the data to a NetApp ASA system.
C. Use NetApp SnapMirror to replicate the cold data to a different, lower-cost StorageGRID cluster.
D. Configure an Information Lifecycle Management (ILM) policy that automatically moves objects older than 90 days to a lower-cost cloud archive tier, such as Amazon S3 Glacier Deep Archive.

Answer: D

NEW QUESTION # 28
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