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Network Appliance NetApp Certified AI Expert Sample Questions:
1. 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.
2. 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.
3. 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
4. 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.
5. 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
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B,C | Question # 3 Answer: B | Question # 4 Answer: B | Question # 5 Answer: A |







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