Network Appliance NS0-901 : NetApp Certified AI Expert Exam

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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

Question #1
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. A custom Python script using the NetApp DataOps Toolkit to trigger the data movement.
B. A manual copy process using an S3 client on a bastion host.
C. NetApp BlueXP copy and sync service to create and manage the data synchronization relationship between the S3 source and the NFS/CIFS destination.
D. NetApp FabricPool to automatically tier the data from StorageGRID to the ASA.
E. NetApp SnapMirror to replicate data from the StorageGRID object store to the ASA's file system.

Question #2
An MLOps engineer is deploying a training pod that requires a high-performance volume. After applying the pod and PVC manifests, the pod remains in a 'Pending' state. The engineer runs
'kubectl describe pod training-pod-7d8c' and sees the following event:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 2m15s
default-scheduler 0/4 nodes are available: 1 node(s) had volume node affinity conflict, 3 node(s) didn't find available persistent volume to bind.
The engineer then inspects the associated PVC and sees its status is also 'Pending'.
What is the most likely cause of this issue?

A. The ONTAP backend is out of available capacity to provision a new volume.
B. The 'storageClassName' specified in the PVC does not match any existing StorageClass managed by Trident.
C. The Trident controller pod has crashed and needs to be restarted.
D. The Kubernetes scheduler is malfunctioning and cannot assign pods to nodes.

Question #3
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. Both Phase 1 and Phase 2 are examples of inferencing.
B. Phase 1 is inferencing, and Phase 2 is training.
C. Phase 1 is training, and Phase 2 is inferencing.
D. Both Phase 1 and Phase 2 are examples of training.

Question #4
An architect is designing a data pipeline for a predictive AI model that will forecast retail sales.
The pipeline must be robust, version-controlled, and efficient.
The proposed data flow is as follows:
1. Ingest: Raw sales data is copied daily from multiple point-of-sale (POS) systems to a central staging area on an on-premises ONTAP cluster.
2. Prepare: The raw data is messy. A data engineering team needs a clean, isolated, and writable copy of the latest daily data to perform cleansing and feature engineering tasks without impacting the original raw data.
3. Train: Once prepared, the cleansed dataset is used to retrain the predictive model on a GPU cluster.
This step must be repeatable with the exact same dataset for compliance.
4. Deploy: The newly trained model is pushed to production inference servers.
Which combination of NetApp technologies best supports this entire predictive AI lifecycle?
(Select all
that apply.)

A. Use NetApp FlexClone to create an instantaneous, space-efficient, writable copy of the daily raw data for the data preparation stage.
B. Use BlueXP backup and recovery to perform the initial data ingest from the POS systems.
C. Use NetApp XCP to efficiently aggregate the raw sales data from POS systems into the central staging area.
D. Use NetApp Snapshots on the prepared dataset volume just before training to create an immutable, point-in-time version for compliance and reproducibility.
E. Use NetApp StorageGRID as the primary storage for the high-performance training stage.
F. Use a RAG architecture for the sales forecasting model.

Question #5
A data scientist is using the NetApp DataOps Toolkit for Python to automate the creation of a new, writable volume for an experiment. The script is intended to clone an existing dataset volume. When the script is executed, it fails with an error.
The relevant portion of the Python script is:
from netapp_dataops.k8s import clone_pvc
clone_pvc(
source_pvc_name="dataset-v1-pvc",
new_pvc_name="experiment-clone-pvc",
namespace="ds-team-1"
)
The script produces the following error in the terminal:
'Error: Failed to clone PVC. Source PVC 'dataset-v1-pvc' not found in namespace 'ds-team-1'.' What is the most likely cause of this error?

A. The source PersistentVolumeClaim (PVC) named 'dataset-v1-pvc' does not exist or is in a different namespace.
B. The Python script is missing the necessary import statement for the toolkit.
C. The NetApp DataOps Toolkit does not support cloning volumes.
D. The Kubernetes cluster does not have NetApp Trident installed.

Solutions: