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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
AI Lifecycle and Deployment	- Operational challenges 1. Scalability and performance optimization 2. Data governance and management - End-to-end AI lifecycle 1. Model training and validation 2. Data ingestion and preparation 3. Model deployment and monitoring
AI Infrastructure and NetApp Solutions	- Converged workloads 1. Shared infrastructure design considerations 2. AI, HPC, and analytics convergence - AI-ready data infrastructure 1. Data pipeline optimization for AI/ML workloads 2. High-performance storage for AI workloads
Industry Use Cases	- AI applications across industries 1. Healthcare AI 2. Digital twins 3. Autonomous systems and agents
AI and Machine Learning Fundamentals	- AI, ML, DL concepts 1. Training, inference, and prediction workflows 2. Differences between AI, machine learning, and deep learning - Algorithm types 1. Reinforcement learning 2. Supervised learning 3. Unsupervised learning

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q89-Q94):

NEW QUESTION # 89
An organization wants to provide its data science team with a secure, on-demand method for using a powerful generative AI model with their private, sensitive corporate data. The solution must ensure that the private data is never exposed to the public internet or the public LLM API endpoint.
The architect is designing a solution using BlueXP.
Which two components are essential for building this secure solution? (Choose 2.)

A. A policy in BlueXP classification to copy all sensitive data to a public cloud bucket for easier access.
B. A private vector database hosted on an on-premises NetApp ONTAP system to store embeddings of the sensitive corporate data.
C. The BlueXP GenAI Toolkit, which acts as a proxy to intercept user prompts and enrich them with data from a local vector database.
D. A direct VPN connection from each data scientist's laptop to the public LLM provider.
E. A BlueXP Connector deployed in a public subnet with a public IP address to allow access to the LLM.

Answer: B,C

NEW QUESTION # 90
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 BlueXP Connector is offline and cannot communicate with the cloud provider.
B. The GenAI toolkit requires a specific license that has not been added to the BlueXP digital wallet.
C. The selected ONTAP version does not support the GenAI toolkit.
D. The Azure service principal associated with the BlueXP Connector lacks the necessary IAM role to create resource groups.

Answer: D

NEW QUESTION # 91
A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
Which tools are best suited for each of these roles?

A. Both the data scientist and the DevOps engineer should use automated production pipelines.
B. The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.
C. The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).
D. Both the data scientist and the DevOps engineer should use Jupyter Notebooks.

Answer: C

NEW QUESTION # 92
An AI platform is suffering from poor performance during distributed training jobs. The training data resides on a single, large NFS volume. Monitoring shows that while the overall network throughput to the storage system is high, individual GPU nodes experience significant I/O wait times, and the single ONTAP volume is becoming a performance bottleneck. The goal is to re- architect the storage layout to maximize read parallelism and throughput for the training cluster.
Which two actions should the architect take to address this performance bottleneck? (Choose 2.)

A. Implement a NetApp FlexGroup volume to spread the dataset across multiple constituent volumes and aggregates.
B. Use NetApp FlexCache to create a local cache of the training data on each compute node.
C. Replace the NFS protocol with iSCSI for all training data access.
D. Increase the number of network ports connected to the storage controller.
E. Enable QoS maximums on the training volume to limit its IOPS.

Answer: A,B

NEW QUESTION # 93
A data scientist on the team wants to run an experiment on a new vector indexing strategy. To do this, they need a temporary, writable copy of the main vector database. They use the NetApp DataOps Toolkit for Python to automate this process.
The following script is executed:
from netapp_dataops.k8s import clone_pvc
clone_pvc(
source_pvc_name="prod-vector-db-pvc",
new_pvc_name="exp-indexing-clone",
namespace="ds-prod"
)
Assuming the source PVC exists and the user has correct permissions, what is the primary benefit of using this method?

A. It creates a full physical copy of the data, providing the best performance isolation.
B. It creates a read-only Snapshot, ensuring the integrity of the experimental data.
C. It automatically migrates the data from the AFF A-Series to the StorageGRID system for the experiment.
D. It uses NetApp FlexClone technology to create a near-instantaneous, space-efficient writable copy of the volume, accelerating the experimental workflow.

Answer: D

NEW QUESTION # 94
......

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