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Network Appliance NS0-901 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Common Challenges | 22% | - Traceability and Optimization
|
| Topic 2: AI Lifecycle | 27% | - Generative AI Concepts
|
| Topic 3: AI Software Architectures | 18% | - MLOps and LLMOps Ecosystems
|
| Topic 4: AI Hardware Architectures | 18% | - NetApp Architectures
|
| Topic 5: AI Overview | 15% | - Algorithm Types
|
Network Appliance NetApp Certified AI Expert Sample Questions:
Which of the following applications use AI in the healthcare industry? (Choose two)
- A. Managing hospital supply chains
- B. Diagnosing diseases
- C. Predicting patient outcomes
- D. Automating financial reporting
Correct Answer: B,C 🗳️
An organization recently suffered a ransomware attack that encrypted several volumes on their primary ONTAP storage system, including a critical volume containing curated training data. The security team needs to implement a solution that can proactively detect and block ransomware- like file I/O patterns and automatically create a secure Snapshot copy before any damage is done.
The current ONTAP configuration is as follows:
ONTAP_Version: 9.12.1
Security_Features: SnapLock (Compliance Mode) on archive volumes
Anti-Virus_Scan: Enabled (Vscan)
Ransomware_Detection: Not configured
Which ONTAP feature should be enabled to provide this proactive, automated protection?
- A. Use BlueXP backup and recovery to back up the volumes to the cloud more frequently.
- B. Enable Autonomous Ransomware Protection (ARP).
- C. Enable Multi-Admin Verification (MAV) on all volumes.
- D. Increase the frequency of scheduled Snapshots.
Correct Answer: B 🗳️
An organization has a core data center with a large AI training cluster and several remote edge locations for data ingest and local inference. The edge locations frequently need access to the latest models trained in the core data center, but WAN bandwidth is limited and can be unreliable.
Users at the edge are reporting slow model loading times.
An architect reviews the data access logs from an edge site:
Timestamp: 2025-07-11T15:30:00Z
Event: Model_Load_Request
Model_Path: nfs://core-filer.example.com/vol/models/latest_model.pkl
Source_IP: 192.168.100.15 (Edge Server)
Destination_IP: 10.1.1.50 (Core Filer)
Status: SUCCESS
Duration: 3600s (60 minutes)
What is the most likely cause of the slow model loading times at the edge?
- A. The core ONTAP filer is using slow, capacity-based disks.
- B. The NFS version used between the core and edge is outdated.
- C. The edge server does not have enough RAM to cache the model effectively.
- D. The model file is being transferred over a slow, high-latency WAN link for every load request.
Correct Answer: D 🗳️
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 Python script is missing the necessary import statement for the toolkit.
- B. The source PersistentVolumeClaim (PVC) named 'dataset-v1-pvc' does not exist or is in a different namespace.
- C. The NetApp DataOps Toolkit does not support cloning volumes.
- D. The Kubernetes cluster does not have NetApp Trident installed.
Correct Answer: B 🗳️
A data science team reports that their Jupyter notebook pod, which was previously working, is now failing to start. The pod's status is 'CrashLoopBackOff'. An MLOps engineer investigates and finds that the pod's PersistentVolumeClaim (PVC) is bound, but the pod logs show a "Permission denied" error when trying to write to its '/data' mount point.
The engineer checks the Trident backend configuration associated with the pod's StorageClass:
apiVersion: trident.netapp.io/v1
kind: TridentBackendConfig
metadata:
name: ontap-nas-eco
spec:
version: 1
storageDriverName: ontap-nas
managementLIF: 10.10.20.5
dataLIF: 10.10.20.10
svm: svm-prod-ds
exportPolicy: read-only-policy
What is the most likely cause of the "Permission denied" error?
- A. The Kubernetes pod has an invalid 'securityContext' that prevents it from writing to any volume.
- B. The 'dataLIF' is configured incorrectly and is unreachable from the Kubernetes nodes.
- C. The Trident backend is configured to use an export policy ('read-only-policy') that does not grant write permissions to the Kubernetes nodes.
- D. The 'storageDriverName' should be 'ontap-san' for all AI workloads.
Correct Answer: C 🗳️
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