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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

1. An AI architect is reviewing the design for a new data lake. The primary requirement is to store petabytes of unstructured data (images, video, sensor logs) in a highly durable, scalable, and cost- effective manner. The data will be accessed via S3 API by various data processing and analytics applications.
The initial design proposes using a traditional Network Attached Storage (NAS) filer with a large number of disks. The architect reviews the proposal:
Proposed_System: Traditional NAS Filer
Protocol: NFSv4
Scalability_Model: Scale-up
Metadata_Handling: Centralized in filer head
Cost_per_GB: Moderate
Why is this proposed system a poor choice for a petabyte-scale data lake?

A) NFS is incapable of storing image or video files.
B) The S3 API cannot be used to access data stored on an NFS file system.
C) A NAS filer cannot be deployed on-premises.
D) A traditional scale-up NAS system will face scalability and cost-effectiveness challenges at the petabyte scale compared to an object storage system.

2. An architect is designing an AI solution for a European hospital chain to analyze patient diagnostic scans. The project is subject to strict GDPR regulations, which mandate that patient data cannot leave the sovereign territory. The application also requires near-instantaneous results for physicians reviewing the scans in the hospital.
Which deployment model best satisfies these security and performance requirements?

A) A multi-cloud strategy using different providers for training and inference to avoid vendor lock-in.
B) A hybrid model using a public cloud for training and on-premises for inference.
C) An on-premises private cloud for training combined with edge deployments in each hospital for inference.
D) A centralized public cloud deployment in North America for maximum scalability.

3. The firm's data science team needs to run a high-priority, interactive model analysis job that requires immediate access to two GPUs. However, all GPUs in the cluster are currently allocated to long-running, lower-priority batch training jobs.
The MLOps platform, Run:AI, shows the following queue status:
JOB_ID | PROJECT | STATUS | PRIORITY | GPU_ALLOCATED
||--|-|
batch_job_1 | team_a | Running | Low | 2
batch_job_2 | team_a | Running | Low | 2
batch_job_3 | team_b | Running | Low | 4
interactive_1| team_c | Pending | High | 2 (requested)
How does the Run:AI platform address this resource contention to allow the high-priority job to run?

A) It automatically terminates all low-priority jobs to free up the entire cluster.
B) It automatically pauses one of the low-priority jobs, saves its state, and allocates its GPUs to the high- priority job, placing the paused job back in the queue.
C) It sends an email notification to the administrator to manually reallocate the GPUs.
D) It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.

4. To meet HIPAA compliance, the first step in the data pipeline is to identify all medical scans that contain embedded PII. The solution must be automated and capable of scanning data in-place on the on-premises ASA system.
Which two technologies should be used to accomplish this identification and tagging task?
(Choose 2.)

A) A manual review process where technicians visually inspect each scan for PII.
B) NetApp BlueXP classification, configured to scan the on-premises ASA working environment.
C) NetApp SnapLock to make the source data immutable before scanning.
D) A custom Python script that uses regular expressions to search file contents.
E) A custom "PII" category within BlueXP classification to identify specific medical record number formats.

5. 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) The PVC will fail because a 'storageClassName' is not explicitly defined.
B) Trident will automatically provision a 50 Gi volume on its default backend and bind it to the PVC.
C) Trident will create a 1 Gi volume, as this is the default size for all PVCs.
D) The PVC will remain in a "Pending" state until a PersistentVolume is manually created.

Solutions: