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Network Appliance NS0-901 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: AI Lifecycle and Deployment | - End-to-end AI lifecycle
|
| Topic 2: AI and Machine Learning Fundamentals | - Algorithm types
|
| Topic 3: Industry Use Cases | - AI applications across industries
|
| Topic 4: AI Infrastructure and NetApp Solutions | - AI-ready data infrastructure
|
Network Appliance NetApp Certified AI Expert Sample Questions:
A data scientist needs to launch a Jupyter notebook as a pod in a Kubernetes cluster. The pod requires a 50 Gi persistent volume for storing datasets and notebooks. The cluster administrator has configured a default Trident StorageClass for general-purpose use. The data scientist has the following PersistentVolumeClaim (PVC) manifest:
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: jupyter-pvc
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 50Gi
When this PVC is applied to the cluster, what will be the result?
- A. Trident will create a 1 Gi volume, as this is the default size for all PVCs.
- B. Trident will automatically provision a 50 Gi volume on its default backend and bind it to the PVC.
- C. The PVC will fail because a 'storageClassName' is not explicitly defined.
- D. The PVC will remain in a "Pending" state until a PersistentVolume is manually created.
Correct Answer: B 🗳️
The architect needs to design an efficient data flow to move curated training sets from the central StorageGRID data lake to the high-performance NetApp ASA system used by the AI cluster. The process must be manageable from a single interface and should be automatable. Which two NetApp technologies should be used to implement this data pipeline stage? (Choose 2.)
- A. NetApp SnapMirror to replicate data from the StorageGRID object store to the ASA's file system.
- B. NetApp BlueXP copy and sync service to create and manage the data synchronization relationship between the S3 source and the NFS/CIFS destination.
- C. A custom Python script using the NetApp DataOps Toolkit to trigger the data movement.
- D. A manual copy process using an S3 client on a bastion host.
- E. NetApp FabricPool to automatically tier the data from StorageGRID to the ASA.
Correct Answer: B,C 🗳️
Which AI technology is used to generate new, never-before-seen content such as images or text?
- A. Reinforcement AI
- B. Generative AI
- C. Supervised AI
- D. Predictive AI
Correct Answer: B 🗳️
A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
Which tools are best suited for each of these roles?
- A. The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.
- B. The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).
- C. Both the data scientist and the DevOps engineer should use automated production pipelines.
- D. Both the data scientist and the DevOps engineer should use Jupyter Notebooks.
Correct Answer: B 🗳️
An AI research team is experiencing slow model training times. Their performance monitoring indicates that the GPUs are frequently idle, waiting for data. They want to implement a single technology change to create a more direct data path between their storage and GPUs.
Their current setup is as follows:
Compute: Server with NVIDIA A100 GPUs
Storage: NetApp AFF A-Series (All-Flash)
Network: 100GbE Ethernet
Data_Path: Storage -> Host CPU/Memory -> GPU Memory
Which technology should the architect recommend to specifically address this data path inefficiency?
- A. GPUDirect Storage
- B. NetApp SnapMirror
- C. A faster CPU in the server
- D. NetApp FabricPool
Correct Answer: A 🗳️
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