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Network Appliance NS0-901 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Cloud and Hybrid Cloud AI Deployment | 18% | - Hybrid and multi-cloud AI architectures - NetApp cloud data services for AI - Cloud-native AI solutions and integration - Data mobility and consistency across environments |
| AI Lifecycle | 27% | - AI governance, ethics, and compliance - AI lifecycle stages: design, training, deployment, monitoring - Predictive vs generative AI - Model training, inference, and optimization - Data preparation and management for AI |
| AI Overview | 15% | - AI, machine learning, and deep learning concepts - AI deployment models: on-premises, cloud, edge - Algorithm types: supervised, unsupervised, reinforcement learning - AI industry use cases and applications - Convergence of AI, high-performance computing, and analytics |
| NetApp AI Solutions and Architecture | 25% | - NetApp AI-ready infrastructure components - Storage architectures for AI workloads - ONTAP integration with AI frameworks - Data management and data pipeline design - Scalability and performance optimization for AI |
| Security, Reliability, and Operations | 15% | - High availability and data protection - Data security and access control for AI - Monitoring, logging, and troubleshooting AI environments - Cost management and efficiency |
Network Appliance NetApp Certified AI Expert Sample Questions:
1. An AI platform is suffering from poor performance during distributed training jobs. The training data resides on a single, large NFS volume. Monitoring shows that while the overall network throughput to the storage system is high, individual GPU nodes experience significant I/O wait times, and the single ONTAP volume is becoming a performance bottleneck. The goal is to re- architect the storage layout to maximize read parallelism and throughput for the training cluster.
Which two actions should the architect take to address this performance bottleneck? (Choose 2.)
A) Replace the NFS protocol with iSCSI for all training data access.
B) Enable QoS maximums on the training volume to limit its IOPS.
C) Implement a NetApp FlexGroup volume to spread the dataset across multiple constituent volumes and aggregates.
D) Use NetApp FlexCache to create a local cache of the training data on each compute node.
E) Increase the number of network ports connected to the storage controller.
2. 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 'vector-db-pod-0' is requesting more GPU resources than are available on any node.
C) 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'.
D) The PersistentVolumeClaim for the pod is in a 'Pending' state.
3. A company is running its AI training workloads on a NetApp AFF A-Series system. To manage costs, they want to automatically move inactive training datasets and older model checkpoints from the high- performance all-flash tier to a lower-cost object storage tier, such as an on- premises StorageGRID or a public cloud bucket. The process must be transparent to the data scientists and not require changes to their scripts or file paths.
Which two NetApp technologies should be combined to achieve this goal? (Choose 2.)
A) NetApp StorageGRID
B) NetApp FlexCache
C) NetApp FabricPool
D) NetApp SnapMirror
E) NetApp XCP
4. An MLOps team is using the BlueXP Workload Factory to automate the deployment of a Jupyter notebook environment for a new data scientist. The deployment job fails. The team reviews the job details in the BlueXP UI.
Job ID: 78a1c-2b3d-4e5f
Service: BlueXP Workload Factory
Action: Deploy JupyterHub on Kubernetes
Status: FAILED
Error Message: "Failed to create PersistentVolumeClaim 'jupyter-pvc-user01'.
User 'service-account- bluexp' does not have permission to 'create' resource
'persistentvolumeclaims' in cluster 'k8s-prod- cluster-01'."
What is the most direct way to resolve this issue?
A) Restart the BlueXP Connector to refresh its permissions.
B) Modify the Kubernetes RBAC (Role-Based Access Control) configuration to grant the 'service- account-bluexp' the necessary permissions to create PVCs in the target cluster.
C) Manually create the PersistentVolumeClaim using 'kubectl'.
D) Increase the storage quota for the 'jupyter-pvc-user01' PVC.
5. A data scientist needs to test a new data normalization technique. To do this, they require an isolated, writable copy of a 50 TB curated simulation dataset that resides on the NetApp ASA system. The operation must be completed as quickly as possible and consume minimal additional storage space. Which NetApp technology is the most appropriate solution for this requirement?
A) NetApp FlexClone
B) NetApp FabricPool
C) NetApp SnapMirror
D) NetApp XCP
Solutions:
| Question # 1 Answer: C,D | Question # 2 Answer: C | Question # 3 Answer: A,C | Question # 4 Answer: B | Question # 5 Answer: A |


