2026 Provide Updated Network Appliance NS0-901 Dumps as Practice Test and PDF [Q45-Q62] – IT Certification Exam Questions & Answers

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2026 Provide Updated Network Appliance NS0-901 Dumps as Practice Test and PDF

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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Common Challenges	22%	– Resource Management 1. Controlling costs and securing storage 2. Sizing storage and compute resources effectively – Traceability and Optimization 1. Ensuring traceability for code, data, and models 2. Optimizing data access and movement 3. Maximizing performance in demanding AI workloads
AI Lifecycle	27%	– Generative AI Concepts 1. Hallucinations 2. Retrieval Augmented Generation (RAG) 3. Fine-tuning – Predictive AI vs. Generative AI 1. Distinction between predictive and generative AI 2. Impact of generative content (text, images, video, decision-making) 3. Large Language Models (LLMs) – Model Development 1. Fine-tuning workflows 2. Model building 3. Inferencing – Data Preparation 1. NetApp BlueXP Classification 2. Data aggregation and cleansing 3. XCP and CopySync
AI Software Architectures	18%	– Scaling and Orchestration 1. Scaling AI workloads with Kubernetes 2. Leveraging BlueXP software tools – MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale – Development Tools 1. NetApp DataOps Toolkit 2. Jupyter notebooks vs. pipelines
AI Hardware Architectures	18%	– Infrastructure Topologies 1. Data aggregation and compute topologies – NetApp Architectures 1. OVX architectures 2. BasePod 3. SuperPOD – Networking and Storage 1. Storage architectures for AI 2. Network protocols for AI workloads
AI Overview	15%	– Machine Learning Fundamentals 1. Describe machine learning benefits 2. Understand the relationship between AI, machine learning, and deep learning – AI Deployment Models 1. Edge 2. Benefits and risks of each model 3. On-premises 4. Cloud – Algorithm Types 1. Unsupervised learning 2. Reinforcement learning 3. Supervised learning – AI Industry Applications 1. Agents 2. Digital twins 3. Healthcare applications – Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads – AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics

Q45. An automotive company runs crash simulations on a dedicated High-Performance Computing (HPC) cluster and trains computer vision models on a separate AI cluster. Data scientists are complaining about the long delays required to move terabytes of simulation output data from the HPC storage to the AI cluster’s storage before they can begin training.
The current data flow is as follows:
HPC Cluster -> –Manual Copy (NFS)–> -> AI Cluster
An architect has been asked to redesign the infrastructure to eliminate this data movement bottleneck.
Which architectural change would be most effective?

Upgrade the network connection between the two storage systems to 200GbE.

Implement a converged data infrastructure where both the HPC and AI clusters access a single, high- performance data lake built on NetApp storage.

Use NetApp XCP to perform the data copy, as it is faster than a standard NFS copy.

Install faster CPUs in the AI cluster’s storage controllers.

Q46. The firm decides to implement a disaster recovery (DR) site for the “Advisor Assistant” application in a secondary data center. The Recovery Point Objective (RPO) is 15 minutes, and the Recovery Time Objective (RTO) is 4 hours. The design must protect both the document data lake and the vector database.
The primary site contains:
– Data Lake: NetApp StorageGRID
– Vector DB: NetApp AFF A-Series
Which combination of technologies and processes provides a complete and robust DR solution?
(Select all that apply.)

Use StorageGRID’s built-in replication rules to replicate object data from the primary site’s grid to a StorageGRID instance at the DR site.

Use NetApp SnapMirror to create an asynchronous replication relationship for the AFF A-Series volume containing the vector database, with a schedule of 10 minutes.

Use the NetApp DataOps Toolkit to manually script the failover process.

Rely on tape backups to be shipped to the DR site in the event of a disaster.

Use BlueXP disaster recovery to orchestrate and automate the failover and failback workflows for the application and its data dependencies.

Use NetApp FlexCache at the DR site to cache data from the primary site.

Q47. An AI infrastructure architect is tasked with designing a solution to address two critical challenges in a large, multi-petabyte AI environment:
1. Cost: A significant portion of the data on the high-performance all-flash storage is inactive but must remain online. The cost of storing this cold data on the performance tier is prohibitive.
2. Traceability: Data scientists need a simple, space-efficient way to version their datasets at key points in their workflow to ensure reproducibility.
The environment consists of NetApp AFF A-Series and NetApp StorageGRID systems.
Which combination of NetApp technologies should the architect implement to solve both challenges simultaneously? (Select all that apply.)

Use NetApp XCP to periodically move cold data from the AFF systems to StorageGRID.

Implement NetApp FabricPool to automatically tier cold data blocks from the AFF systems to StorageGRID.

Use NetApp SnapMirror to replicate volumes from the AFF systems to StorageGRID for archival.

Train data scientists to use NetApp Snapshots to create point-in-time, read-only versions of their data volumes.

Implement NetApp FlexClone to create full, writable copies of datasets for each experiment.

Use BlueXP backup and recovery to create backups on StorageGRID, then delete the original volumes from the AFF systems.

Q48. Given the company’s goal of combining physics-based simulations with AI-driven analytics on a shared data foundation, which industry trend does this project best represent?

The separation of AI and HPC into dedicated, air-gapped environments.

The exclusive use of public cloud resources for all computational tasks.

The convergence of AI, High-Performance Computing (HPC), and analytics on a unified data infrastructure.

The replacement of all physical testing with digital simulations.

Q49. Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)

KubeFlow

TensorFlow Extended

Google VertexAI

RunAI

Q50. 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.)

Replace the NFS protocol with iSCSI for all training data access.

Implement a NetApp FlexGroup volume to spread the dataset across multiple constituent volumes and aggregates.

Increase the number of network ports connected to the storage controller.

Use NetApp FlexCache to create a local cache of the training data on each compute node.

Enable QoS maximums on the training volume to limit its IOPS.

Q51. A media company is building a new generative AI service. The project has two main components:
1. Data Lake & Fine-Tuning: A 300 TB repository of unstructured data (videos, images, text) stored as objects will be used to fine-tune a foundational model. This process requires a scalable, cost-effective storage solution that can integrate with cloud-native data processing tools like Apache Spark.
2. Inference & RAG: The fine-tuned model will be used in a customer-facing application that leverages Retrieval-Augmented Generation (RAG). To ensure low-latency responses, the RAG component requires extremely fast lookups from a 10 TB vector database.
The company needs a solution that optimizes both cost and performance for this entire lifecycle.
Which combination of NetApp technologies provides the most appropriate solution for this scenario?

Use NetApp E-Series for the data lake and a NetApp ASA system for the vector database.

Use NetApp StorageGRID for the data lake and a NetApp ASA system for the vector database.

Use a single, large NetApp ASA system for both the object data lake and the vector database.

Use NetApp Cloud Volumes ONTAP for the data lake and NetApp StorageGRID for the vector database.

Q52. An architect is designing an AI solution for a European hospital chain to analyze patient diagnostic scans. The project is subject to strict GDPR regulations, which mandate that patient data cannot leave the sovereign territory. The application also requires near-instantaneous results for physicians reviewing the scans in the hospital.
Which deployment model best satisfies these security and performance requirements?

A centralized public cloud deployment in North America for maximum scalability.

A hybrid model using a public cloud for training and on-premises for inference.

An on-premises private cloud for training combined with edge deployments in each hospital for inference.

A multi-cloud strategy using different providers for training and inference to avoid vendor lock-in.

Q53. An architect is designing a cost-optimized storage solution for a large AI environment that has two distinct data temperature profiles:
1. Hot Data (200 TB): Actively used training sets and models requiring all-flash performance.
2. Cold Data (2 PB): Inactive, archived datasets and old model versions that must remain online but are infrequently accessed.
The solution must automatically manage data placement based on activity, without requiring manual intervention or changes to file paths. The environment consists of an on-premises data center and a public cloud account.
Which combination of NetApp technologies provides the most complete and cost-effective solution? (Select all that apply.)

Deploy a NetApp AFF A-Series system on-premises to serve as the high-performance tier for the hot data.

Deploy NetApp StorageGRID on-premises to serve as a cost-effective capacity tier for the cold data.

Use NetApp FabricPool to automatically and transparently tier cold data blocks from the AFF A- Series to the StorageGRID system.

Use NetApp SnapMirror to create a full copy of the 2 PB of cold data on the AFF A-Series for faster access if needed.

Use NetApp XCP to manually script the daily movement of cold files from the AFF system to StorageGRID.

Deploy a single, large Cloud Volumes ONTAP instance to hold both the hot and cold data tiers.

Q54. 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?

The source PVC ‘training-data-v2’ does not exist.

The Python script is attempting to use the ‘clone_pvc’ function, but it was not imported from the
‘netapp_dataops.k8s’ library.

The NetApp DataOps Toolkit is not installed in the container image used for this pipeline step.

The Kubernetes cluster is not running NetApp Trident.

Q55. Which of the following best describes the difference between data lakes, data warehouses, and lakehouses?

Data lakes store structured data, data warehouses store unstructured data, and lakehouses store only real-time data.

Data lakes store raw, unstructured data, data warehouses store structured data, and lakehouses combine the features of both.

Data lakes store metadata, data warehouses store transaction data, and lakehouses store archival data.

Data lakes store data in cloud storage, data warehouses store it in traditional databases, and lakehouses store it in external drives.

Q56. Given the firm’s requirements for using a private, constantly updated knowledge base and the strict mandate for data traceability, which AI architecture is the most appropriate foundation for the “Advisor Assistant” chatbot?

A predictive AI model trained to forecast market trends.

A fine-tuning architecture where a base LLM is continuously retrained on the entire document repository.

A Retrieval-Augmented Generation (RAG) architecture that retrieves relevant context from a local vector database to enrich prompts sent to the LLM.

A standalone LLM deployed in an air-gapped environment with no access to the document repository.

Q57. The architect is designing the complete, automated data pipeline from the on-premises data center to the Azure cloud for this medical imaging project. The design must prioritize security, efficiency, and reproducibility.
Which sequence of actions provides the most robust and automated solution?

Manually copy raw data to Azure, run a cloud-based script to anonymize it, then start training.

Use BlueXP classification to scan the on-prem data; use a DataOps Toolkit script to trigger a FlexClone of the source volume for anonymization; use SnapMirror to replicate the anonymized clone to Cloud Volumes ONTAP; then begin training in Azure.

Use SnapMirror to replicate the raw, sensitive data to Azure first, then run BlueXP classification in the cloud to find PII.

Use FabricPool to automatically tier all raw data to Azure Blob storage, then mount the blob container to the training VMs.

Q58. The firm’s data science team needs to run a high-priority, interactive model analysis job that requires immediate access to two GPUs. However, all GPUs in the cluster are currently allocated to long-running, lower-priority batch training jobs.
The MLOps platform, Run:AI, shows the following queue status:
JOB_ID | PROJECT | STATUS | PRIORITY | GPU_ALLOCATED
||–|-|
batch_job_1 | team_a | Running | Low | 2
batch_job_2 | team_a | Running | Low | 2
batch_job_3 | team_b | Running | Low | 4
interactive_1| team_c | Pending | High | 2 (requested)
How does the Run:AI platform address this resource contention to allow the high-priority job to run?

It automatically terminates all low-priority jobs to free up the entire cluster.

It automatically pauses one of the low-priority jobs, saves its state, and allocates its GPUs to the high- priority job, placing the paused job back in the queue.

It sends an email notification to the administrator to manually reallocate the GPUs.

It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.

Q59. 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?

Re-run the vector database embedding job.

Modify the NFS export policy on the ‘vol_kb’ volume on the on-premises ONTAP cluster to grant access to the BlueXP Connector.

Fine-tune the LLM with the latest data instead of using the RAG system.

Check the IAM permissions for the role associated with the S3 bucket.

Project A is a model building task and requires a significantly larger and more powerful compute and storage infrastructure than Project B.

Project B is a fine-tuning task, which leverages an existing model and requires less data and compute resources than building a model from scratch.

Both projects should use NetApp StorageGRID as the primary storage for training to ensure low latency.

Project A requires only a large amount of data, while Project B requires only a large amount of code.

Both projects are examples of fine-tuning and have similar infrastructure needs.

Q61. 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?

Phase 1 is inferencing, and Phase 2 is training.

Phase 1 is training, and Phase 2 is inferencing.

Both Phase 1 and Phase 2 are examples of training.

Both Phase 1 and Phase 2 are examples of inferencing.

Q62. 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.)

The BlueXP GenAI Toolkit, which acts as a proxy to intercept user prompts and enrich them with data from a local vector database.

A BlueXP Connector deployed in a public subnet with a public IP address to allow access to the LLM.

A private vector database hosted on an on-premises NetApp ONTAP system to store embeddings of the sensitive corporate data.

A direct VPN connection from each data scientist’s laptop to the public LLM provider.

A policy in BlueXP classification to copy all sensitive data to a public cloud bucket for easier access.

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