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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Cloud and Hybrid Cloud AI Deployment	18%	- Data mobility and consistency across environments - Hybrid and multi-cloud AI architectures - NetApp cloud data services for AI - Cloud-native AI solutions and integration
AI Overview	15%	- AI, machine learning, and deep learning concepts - Convergence of AI, high-performance computing, and analytics - AI industry use cases and applications - Algorithm types: supervised, unsupervised, reinforcement learning - AI deployment models: on-premises, cloud, edge
AI Lifecycle	27%	- Data preparation and management for AI - Predictive vs generative AI - AI governance, ethics, and compliance - AI lifecycle stages: design, training, deployment, monitoring - Model training, inference, and optimization
NetApp AI Solutions and Architecture	25%	- NetApp AI-ready infrastructure components - Scalability and performance optimization for AI - ONTAP integration with AI frameworks - Data management and data pipeline design - Storage architectures for AI workloads
Security, Reliability, and Operations	15%	- Monitoring, logging, and troubleshooting AI environments - Data security and access control for AI - Cost management and efficiency - High availability and data protection

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q15-Q20):

NEW QUESTION # 15
An AI platform administrator is trying to deploy a new GenAI toolkit using the BlueXP Workload Factory. The deployment fails, and the administrator examines the API response from BlueXP.
{
"jobId": "we-deploy-genai-987zy",
"status": "FAILED",
"statusCode": 403,
"message":
"Forbidden: The service principal or user account used by the Connector does not have the required permissions on the target subscription to create a new resource group.
Required permission: 'Microsoft.Resources/subscriptions/resourcegroups/write'." } Based on this API response, what is the root cause of the deployment failure?

A. The GenAI toolkit requires a specific license that has not been added to the BlueXP digital wallet.
B. The Azure service principal associated with the BlueXP Connector lacks the necessary IAM role to create resource groups.
C. The selected ONTAP version does not support the GenAI toolkit.
D. The BlueXP Connector is offline and cannot communicate with the cloud provider.

Answer: B

NEW QUESTION # 16
An MLOps engineer is troubleshooting a failed Kubeflow pipeline step. The step was designed to create a clone of a dataset for a training job using the NetApp DataOps Toolkit. The pod logs for the failed pipeline step show the following:
Traceback (most recent call last):
File "create_clone.py", line 15, in <module>
clone_pvc(source_pvc_name="training-data-v2", new_pvc_name="train-job-34a-data") NameError: name 'clone_pvc' is not defined The engineer reviews the Python script for the pipeline step:
# create_clone.py
import os
from netapp_dataops.k8s import create_pvc
# Other code
print("Cloning source dataset for training run...")
clone_pvc(
source_pvc_name="training-data-v2",
new_pvc_name="train-job-34a-data"
)
print("Clone created successfully.")
What is the cause of the error?

A. The Python script is attempting to use the 'clone_pvc' function, but it was not imported from the
'netapp_dataops.k8s' library.
B. The source PVC 'training-data-v2' does not exist.
C. The Kubernetes cluster is not running NetApp Trident.
D. The NetApp DataOps Toolkit is not installed in the container image used for this pipeline step.

Answer: A

NEW QUESTION # 17
To comply with the security mandate, the architect must design a process to prevent client PII from the portfolio database from ever being included in the context sent to the LLM. Which two actions are required to build a robust and automated solution for this? (Choose 2.)

A. Implement a data preparation step in the RAG pipeline that programmatically masks or excludes any data fields tagged as PII before the context is constructed.
B. Use BlueXP classification to perform an initial scan of the portfolio database to identify and tag all columns containing PII.
C. Rely on a disclaimer in the chatbot UI telling advisors not to enter queries related to specific clients.
D. Fine-tune the LLM on a sanitized dataset to teach it to ignore PII.
E. Move the entire client portfolio database to an encrypted, air-gapped system.

Answer: A,B

NEW QUESTION # 18
An architect is designing a fully automated, end-to-end MLOps pipeline on Kubernetes for a computer vision use case. The pipeline must handle everything from data versioning to model deployment.
The required pipeline stages are:
1. Data Versioning: Create a new, immutable version of the master dataset for the pipeline run.
2. Data Preparation: Launch a pod to run a preprocessing script on the versioned data.
3. Model Training: Launch a distributed training job that reads the prepared data from a highperformance volume.
4. Model Deployment: Push the trained model to a production inference service.
Which combination of NetApp and Kubernetes technologies provides the most effective and automated solution for this entire pipeline?

A. Use NetApp SnapMirror for data versioning and manually create hostPath volumes for each pipeline stage.
B. Use NetApp XCP to copy the data for each stage and configure static PersistentVolumes for each pod.
C. Manually create a NetApp Snapshot via System Manager before each pipeline run, and use the NetApp DataOps Toolkit only for the training stage.
D. Use the NetApp DataOps Toolkit to create a Snapshot of the source data volume (for versioning), then create a FlexClone PVC from the snapshot for the preparation stage, and finally create a FlexGroup PVC for the training stage.
E. Use a single, large ReadWriteMany PVC for all stages to simplify the pipeline configuration.

Answer: D

NEW QUESTION # 19
A national research laboratory is investing in a turnkey AI infrastructure solution. Their primary goal is to eliminate the complexity and risk of designing and integrating the compute, network, and storage components themselves. The solution must be pre-validated by the vendors to deliver predictable, linear performance as they scale from one to multiple compute nodes. Which two options represent this type of pre-validated, converged infrastructure solution for AI? (Choose two)

A. NetApp AIPod, which integrates NVIDIA DGX servers with NetApp storage and networking.
B. A custom-built server rack with components sourced from various different vendors.
C. A Cisco and NetApp FlexPod for AI solution.
D. A single NetApp FAS system connected directly to a single server.
E. A public cloud provider's general-purpose virtual machine instances.

Answer: A,C

NEW QUESTION # 20
......

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