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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. Data ingestion and preparation 2. Model deployment and monitoring 3. Model training and validation
Industry Use Cases	- AI applications across industries 1. Digital twins 2. Healthcare AI 3. Autonomous systems and agents
AI and Machine Learning Fundamentals	- Algorithm types 1. Supervised learning 2. Unsupervised learning 3. Reinforcement learning - AI, ML, DL concepts 1. Training, inference, and prediction workflows 2. Differences between AI, machine learning, and deep learning
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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q88-Q93):

NEW QUESTION # 88
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. Enable QoS maximums on the training volume to limit its IOPS.
B. Implement a NetApp FlexGroup volume to spread the dataset across multiple constituent volumes and aggregates.
C. Use NetApp FlexCache to create a local cache of the training data on each compute node.
D. Increase the number of network ports connected to the storage controller.
E. Replace the NFS protocol with iSCSI for all training data access.

Answer: B,C

NEW QUESTION # 89
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 automatically migrates the data from the AFF A-Series to the StorageGRID system for the experiment.
C. It uses NetApp FlexClone technology to create a near-instantaneous, space-efficient writable copy of the volume, accelerating the experimental workflow.
D. It creates a read-only Snapshot, ensuring the integrity of the experimental data.

Answer: C

NEW QUESTION # 90
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 source volume at the edge location has become corrupted.
B. The SnapMirror relationship is broken because the destination volume at the core data center has run out of capacity.
C. The network connection between the edge and the core is unreliable.
D. The BlueXP Connector does not have the correct permissions to manage the SnapMirror relationship.

Answer: B

NEW QUESTION # 91
A development team is building a generative AI application that must answer questions based on a constantly changing internal knowledge base of company documents. They want to provide the model with up-to-date information without altering its core weights and capabilities.
Which approach is most suitable for this requirement?

A. Fine-tuning, which retrains the model's last few layers on the new documents.
B. Unsupervised learning, which clusters the documents into topics for the model to browse.
C. Predictive AI, which analyzes the documents to forecast future questions.
D. Retrieval-Augmented Generation (RAG), which fetches relevant information from the documents at query time to inform the model's response.

Answer: D

NEW QUESTION # 92
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 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.
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 NetApp SnapMirror for data versioning and manually create hostPath volumes for each pipeline stage.
E. Use a single, large ReadWriteMany PVC for all stages to simplify the pipeline configuration.

Answer: A

NEW QUESTION # 93
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