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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q31-Q36):

NEW QUESTION # 31
Which of the following describes the impact of generative AI in content creation?

A. Generative AI analyzes data without generating content.
B. Generative AI predicts customer preferences without creating new content.
C. Generative AI only processes pre-existing content.
D. Generative AI can create new content such as text, images, and videos.

Answer: D

NEW QUESTION # 32
An AI training job is running slower than expected. The infrastructure team suspects a compute bottleneck. The job involves processing high-resolution images with a complex convolutional neural network (CNN). They review the logs from the training script.
Epoch 1/100 - 3600s - loss: 1.253 - acc: 0.54
...
CPU_Utilization_During_Epoch: 99% (all cores)
GPU_0_Utilization_During_Epoch: 8%
GPU_1_Utilization_During_Epoch: 7%
...
Epoch 2/100 - 3610s - loss: 1.102 - acc: 0.61
What is the most likely cause of the poor training performance?

A. The training script is CPU-bound, likely performing data augmentation or preprocessing on the CPU instead of offloading it to the GPUs.
B. The model is too simple and does not effectively utilize the GPU's parallel processing capabilities.
C. The network is saturated, preventing the GPUs from receiving data.
D. The storage system cannot deliver data fast enough to the compute node.

Answer: A

NEW QUESTION # 33
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 NetApp DataOps Toolkit is not installed in the container image used for this pipeline step.
C. The source PVC 'training-data-v2' does not exist.
D. The Kubernetes cluster is not running NetApp Trident.

Answer: A

NEW QUESTION # 34
The firm has acquired a competitor, and the volume of proprietary documents for the "Advisor Assistant" is expected to triple, exceeding the capacity of the current StorageGRID data lake. The architect needs to expand the data lake's capacity non-disruptively.
The current StorageGRID status is:
System_Health: Nominal
Node_Count: 6 (3 Storage Nodes, 3 Admin/Gateway Nodes)
Usable_Capacity: 1.5 PB
Used_Capacity: 1.4 PB (93%)
What is the standard procedure for scaling the capacity of the on-premises NetApp StorageGRID system?

A. Use FabricPool to tier the excess data from StorageGRID to a public cloud provider.
B. Replace the disks in the existing storage nodes with higher-capacity drives.
C. Add new storage nodes to the existing StorageGRID system and execute an expansion procedure.
D. Provision a new, separate StorageGRID system and use BlueXP copy and sync to move the data.

Answer: C

NEW QUESTION # 35
An AI operations team is troubleshooting why their RAG-based chatbot is providing outdated information. They have confirmed that the vector database embedding process is functioning correctly, but suspect an issue with the initial data synchronization that moves the knowledge base from an on- premises ONTAP file share to a cloud staging bucket.
They inspect the relevant BlueXP copy and sync job and find the following details:
Service: BlueXP copy and sync
Relationship_Name: KB_Sync_to_Vector_Staging
Source: nfs://ontap-cluster-1/vol_kb/docs
Destination: s3://vector-staging-bucket-89a3/latest/
Last_Sync_Status: FAILED
Last_Sync_Time: 2025-07-11T02:00:15Z
Error_Message: "Authentication error:
Unable to access source.
Check export policy on 'vol_kb'."
Based on this information, what is the most direct solution to fix the data pipeline?

A. Fine-tune the LLM with the latest data instead of using the RAG system.
B. Modify the NFS export policy on the 'vol_kb' volume on the on-premises ONTAP cluster to grant access to the BlueXP Connector.
C. Check the IAM permissions for the role associated with the S3 bucket.
D. Re-run the vector database embedding job.

Answer: B

NEW QUESTION # 36
......

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