Exam NS0-901 Topic - NS0-901 New Dumps Questions

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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Cloud and Hybrid Cloud AI Deployment	18%	- Hybrid and multi-cloud AI architectures - Data mobility and consistency across environments - Cloud-native AI solutions and integration - NetApp cloud data services for AI
Topic 2: AI Overview	15%	- AI industry use cases and applications - Algorithm types: supervised, unsupervised, reinforcement learning - AI, machine learning, and deep learning concepts - Convergence of AI, high-performance computing, and analytics - AI deployment models: on-premises, cloud, edge
Topic 3: NetApp AI Solutions and Architecture	25%	- Data management and data pipeline design - Storage architectures for AI workloads - Scalability and performance optimization for AI - ONTAP integration with AI frameworks - NetApp AI-ready infrastructure components
Topic 4: Security, Reliability, and Operations	15%	- Data security and access control for AI - High availability and data protection - Monitoring, logging, and troubleshooting AI environments - Cost management and efficiency
Topic 5: AI Lifecycle	27%	- Model training, inference, and optimization - Predictive vs generative AI - AI lifecycle stages: design, training, deployment, monitoring - AI governance, ethics, and compliance - Data preparation and management for AI

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
A financial services company is required by regulators to be able to trace any version of their deployed fraud detection model back to the exact dataset and source code commit used to train it.
The current MLOps workflow is as follows:
Code_Repository: Git (commit hash: a1b2c3d4)
Dataset_Location: /vol/prod_data/fraud_dataset_v3
Storage_System: NetApp ONTAP 9
Model_Output: /vol/models/fraud_model_v3.2
Which NetApp technology should be used to create an immutable, point-in-time, and space- efficient copy of the dataset that can be linked to the specific code commit and model version?

A. NetApp FabricPool
B. NetApp Snapshots
C. NetApp FlexClone
D. NetApp SnapMirror

Answer: B

NEW QUESTION # 14
To meet HIPAA compliance, the first step in the data pipeline is to identify all medical scans that contain embedded PII. The solution must be automated and capable of scanning data in-place on the on-premises ASA system.
Which two technologies should be used to accomplish this identification and tagging task?
(Choose 2.)

A. A custom Python script that uses regular expressions to search file contents.
B. NetApp BlueXP classification, configured to scan the on-premises ASA working environment.
C. NetApp SnapLock to make the source data immutable before scanning.
D. A manual review process where technicians visually inspect each scan for PII.
E. A custom "PII" category within BlueXP classification to identify specific medical record number formats.

Answer: B,E

NEW QUESTION # 15
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. Use NetApp SnapMirror for data versioning and manually create hostPath volumes for each pipeline stage.
D. Manually create a NetApp Snapshot via System Manager before each pipeline run, and use the NetApp DataOps Toolkit only for the training stage.
E. Use a single, large ReadWriteMany PVC for all stages to simplify the pipeline configuration.

Answer: A

NEW QUESTION # 16
An organization is developing a new AI-powered application. The initial phase involves feeding a curated 50 TB dataset of labeled images into a complex neural network, allowing the model to learn and adjust its internal parameters over millions of iterations. The second phase involves deploying this finalized model to a web service where it will process single, user-uploaded images and return a classification in real-time.
Which statement accurately describes these two phases?

A. Phase 1 is training, and Phase 2 is inferencing.
B. Both Phase 1 and Phase 2 are examples of inferencing.
C. Phase 1 is inferencing, and Phase 2 is training.
D. Both Phase 1 and Phase 2 are examples of training.

Answer: A

NEW QUESTION # 17
An architect is designing a global infrastructure for a company that develops AI for autonomous vehicles.
The design must accommodate three distinct locations and functions:
1. Edge (Test Tracks): Fleets of test cars generate 100s of TBs of sensor data per day. This data must be ingested locally with high performance.
2. Core (Primary Data Center): The raw data from all edge sites must be aggregated here. This location houses the primary data lake and the main GPU cluster for large-scale model training.
3. Cloud (Public Cloud Provider): Data scientists want to use cloud-native tools for experimental data processing and model development. They also need a cost-effective location for long-term archiving of raw data.
Which combination of deployment locations and NetApp technologies creates the most logical and efficient end-to-end solution?

A. Use NetApp E-Series at the edge, NetApp ASA at the core, and NetApp StorageGRID in the cloud.
Use SnapMirror to move data between all three tiers.
B. Deploy a single, global NetApp StorageGRID across all three locations to act as a unified data plane.
C. Use NetApp ONTAP systems at the edge and core, and Cloud Volumes ONTAP in the public cloud.
Use SnapMirror to replicate data from edge to core, and FabricPool to tier data from the core to the cloud.
D. Use Cloud Volumes ONTAP at the edge, NetApp StorageGRID at the core, and on-premises ONTAP for cloud archive.

Answer: C

NEW QUESTION # 18
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