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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Topic 1: AI and Machine Learning Fundamentals	- Algorithm types 1. Reinforcement learning 2. Unsupervised learning 3. Supervised learning - AI, ML, DL concepts 1. Differences between AI, machine learning, and deep learning 2. Training, inference, and prediction workflows
Topic 2: AI Infrastructure and NetApp Solutions	- AI-ready data infrastructure 1. Data pipeline optimization for AI/ML workloads 2. High-performance storage for AI workloads - Converged workloads 1. AI, HPC, and analytics convergence 2. Shared infrastructure design considerations
Topic 3: AI Lifecycle and Deployment	- End-to-end AI lifecycle 1. Model training and validation 2. Model deployment and monitoring 3. Data ingestion and preparation - Operational challenges 1. Scalability and performance optimization 2. Data governance and management
Topic 4: Industry Use Cases	- AI applications across industries 1. Autonomous systems and agents 2. Digital twins 3. Healthcare AI

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q18-Q23):

NEW QUESTION # 18
A research lab uses a fleet of autonomous drones to collect high-resolution aerial imagery for agricultural analysis. The drones land at a remote edge location and offload their data. The AI models for image analysis are trained at a central data center. The team is using NetApp SnapMirror to replicate the data from the edge to the core. However, the data scientists are complaining that the datasets arriving at the data center are often incomplete or corrupted.
An administrator reviews the SnapMirror configuration and status via the BlueXP API:
{
"source": { "workingEnvironmentId": "OnPrem-Edge-Filer-1", "volumeName": "drone_data_raw" },
"destination": { "workingEnvironmentId": "Core-Datacenter-A800", "volumeName":
"drone_data_replicated" },
"mirrorState": "broken-off",
"relationshipStatus": "idle",
"unhealthyReason": "Transfer failed. Destination volume is out of space.", "lastTransferInfo": {
"transferError": "No space left on device"
}
}
What is the direct cause of the incomplete datasets at the data center?

A. The network connection between the edge and the core is unreliable.
B. The source volume at the edge location has become corrupted.
C. The BlueXP Connector does not have the correct permissions to manage the SnapMirror relationship.
D. The SnapMirror relationship is broken because the destination volume at the core data center has run out of capacity.

Answer: D

NEW QUESTION # 19
The company's finance department mandates a cost-control strategy for the petabyte-scale StorageGRID data lake. The analysis shows that 80% of the simulation data is not accessed after
90 days but must be retained for five years for regulatory compliance. Which StorageGRID feature should the architect use to automatically reduce the long-term storage costs for this inactive data?

A. Implement a FabricPool policy to tier the data to a NetApp ASA system.
B. Use NetApp SnapMirror to replicate the cold data to a different, lower-cost StorageGRID cluster.
C. Configure an Information Lifecycle Management (ILM) policy that automatically moves objects older than 90 days to a lower-cost cloud archive tier, such as Amazon S3 Glacier Deep Archive.
D. Enable deduplication and compression on all StorageGRID storage nodes.

Answer: C

NEW QUESTION # 20
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.

Answer: D

NEW QUESTION # 21
An AI architect needs to design a complete, end-to-end data pipeline for a new generative AI application at a financial services firm. The application will allow internal analysts to query a massive, 500 TB archive of historical market data and reports to generate summaries. The firm has the following environment and requirements:
Data_Sources: A mix of on-premises ONTAP filers and StorageGRID S3 buckets.
Requirement_1: All queries must be answered using only the private data archive.
Requirement_2: All generated summaries must provide citations to the source reports.
Requirement_3: All data containing client PII must be identified and excluded from the LLM context.
Requirement_4: The solution must be cost-effective for the large, mostly-read data archive.
Which set of actions and technologies constitutes the most robust and compliant solution?
(Select all that apply.)

A. During the RAG retrieval step, filter out any documents tagged as containing PII by BlueXP classification before sending them to the LLM.
B. Implement a Retrieval-Augmented Generation (RAG) architecture to meet the requirements for private data usage and source citation.
C. Use SnapMirror to replicate the StorageGRID data lake to a high-performance NetApp ASA system for faster query performance.
D. Use NetApp XCP to perform a one-time migration of all data from the ONTAP filers to the StorageGRID data lake.
E. Deploy BlueXP classification to scan the entire StorageGRID data lake to identify and tag all files containing PII.
F. Fine-tune a foundation model on the entire 500 TB dataset to ensure it understands the financial context.

Answer: A,B,D,E

NEW QUESTION # 22
The data science team in Azure reports that training jobs are taking longer than expected. An analysis of the Cloud Volumes ONTAP instance in Azure shows that the instance type is undersized for the I/O demands of the training workload. The architect needs to change the Azure VM instance type for the Cloud Volumes ONTAP system to a more powerful one.
The current configuration is:
Cloud_Provider: Azure
ONTAP_System: Cloud Volumes ONTAP (Single Node)
Current_Instance_Type: Standard_DS3_v2
Target_Instance_Type: Standard_E8s_v4
What is the most direct method to perform this operation using NetApp's management tools?

A. Use the Azure portal to manually change the VM instance type.
B. Terminate the existing Cloud Volumes ONTAP instance and deploy a new one with the target instance type, then restore data from backup.
C. From the BlueXP Canvas, select the Cloud Volumes ONTAP working environment and use the
"Change Instance Type" action in the Features panel.
D. SSH to the Cloud Volumes ONTAP instance and run an ONTAP command to modify the underlying VM.

Answer: C

NEW QUESTION # 23
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