Authentic NS0-901 Dumps – Free PDF Questions to Pass [Q27-Q47]

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Authentic NS0-901 Dumps – Free PDF Questions to Pass

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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
AI and Machine Learning Fundamentals	– AI, ML, DL concepts 1. Differences between AI, machine learning, and deep learning 2. Training, inference, and prediction workflows – Algorithm types 1. Unsupervised learning 2. Reinforcement learning 3. Supervised learning
Industry Use Cases	– AI applications across industries 1. Autonomous systems and agents 2. Healthcare AI 3. Digital twins
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
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. Data governance and management 2. Scalability and performance optimization

NO.27 To comply with the security mandate, the architect must design a process to prevent client PII from the portfolio database from ever being included in the context sent to the LLM. Which two actions are required to build a robust and automated solution for this? (Choose 2.)

Use BlueXP classification to perform an initial scan of the portfolio database to identify and tag all columns containing PII.

Fine-tune the LLM on a sanitized dataset to teach it to ignore PII.

Implement a data preparation step in the RAG pipeline that programmatically masks or excludes any data fields tagged as PII before the context is constructed.

Rely on a disclaimer in the chatbot UI telling advisors not to enter queries related to specific clients.

Move the entire client portfolio database to an encrypted, air-gapped system.

NO.28 Which of the following platforms provides tools for model training and deployment specifically for AI workloads?

Google VertexAI

Domino Data Labs

RunAI

All of the above

NO.29 What is the primary reason for implementing a hybrid cloud architecture in this medical imaging scenario?

To reduce the overall cost of storage by keeping all data in the public cloud.

To leverage the powerful, elastic GPU compute resources available in the public cloud for model training while keeping sensitive source data on-premises to meet security and HIPAA compliance requirements.

To simplify network management by using a single, global virtual network spanning the on- premises and cloud environments.

To exclusively use cloud-native AI services, eliminating the need for any on-premises hardware.

NO.30 An internal audit requires the firm to prove the exact state of the financial product documentation that was used by the chatbot to answer a specific query from last Tuesday. The query log shows the request was processed at ‘2025-07-11T14:30:00Z’. The data pipeline that updates the vector database runs daily at midnight.
Which NetApp technology allows the team to instantly access an immutable, point-in-time version of the vector database volume as it existed on that specific day?

NetApp SnapMirror

NetApp FabricPool

NetApp Snapshots

NetApp FlexClone

NO.31 A team has deployed a Retrieval-Augmented Generation (RAG) system to answer customer queries. Recently, users have complained that the answers provided by the chatbot are outdated and do not reflect the latest product updates. An architect investigates and finds the following status log from the RAG pipeline’s data ingestion monitor.
Timestamp: 2025-07-11T14:00:00Z
System: RAG Pipeline Monitor
Status: WARNING
Message: Vector DB freshness check failed.
Source data appears stale.
Vector_DB_Last_Update: 2025-06-10T08:00:00Z
Knowledge_Base_Last_Modified: 2025-07-11T13:15:00Z
Data_Sync_Service: BlueXP copy and sync
Sync_Job_Status: Succeeded
Based on the log, what is the most likely cause of the outdated answers?

The LLM needs to be fine-tuned with the new product information.

The BlueXP copy and sync service is failing to copy data to the staging area.

The process that converts staged documents into vectors and updates the vector database is not running.

The knowledge base itself has not been updated with the latest product information.

NO.32 A data scientist needs to launch a Jupyter notebook as a pod in a Kubernetes cluster. The pod requires a 50 Gi persistent volume for storing datasets and notebooks. The cluster administrator has configured a default Trident StorageClass for general-purpose use. The data scientist has the following PersistentVolumeClaim (PVC) manifest:
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: jupyter-pvc
spec:
accessModes:
– ReadWriteOnce
resources:
requests:
storage: 50Gi
When this PVC is applied to the cluster, what will be the result?

The PVC will fail because a ‘storageClassName’ is not explicitly defined.

The PVC will remain in a “Pending” state until a PersistentVolume is manually created.

Trident will automatically provision a 50 Gi volume on its default backend and bind it to the PVC.

Trident will create a 1 Gi volume, as this is the default size for all PVCs.

NO.33 An MLOps team uses a variety of platforms to manage their AI workloads. They need to understand the primary function of each tool within their ecosystem. Which statement best describes the role of an MLOps/LLMOps platform like Kubeflow or Run:AI?

They are specialized storage systems designed to hold large datasets for training.

They are integrated development environments (IDEs) used exclusively for writing Python code.

They are orchestration and management platforms that automate and streamline the entire AI/ML lifecycle, from data preparation and model training to deployment and monitoring.

They are networking protocols designed to accelerate data transfer between GPUs.

NO.34 Due to the success of the “Advisor Assistant,” the number of concurrent users is expected to double in the next quarter. The existing Kubernetes cluster is running at 80% of its GPU capacity during peak hours. The architect must propose a plan to scale the compute infrastructure to handle the increased load.
Which two strategies represent the most effective and scalable solutions? (Choose 2.)

Replace all existing GPUs with the next-generation model to increase the performance of each node.

Vertically scale the existing nodes by adding more RAM and CPU cores to each one.

Horizontally scale the cluster by adding new GPU-equipped nodes.

Configure the Kubernetes Cluster Autoscaler to automatically add new nodes to the cluster from a predefined node pool when GPU demand exceeds current capacity.

Manually deploy all new application replicas to a single, new, very large “super-node”.

NO.35 Which of the following are typical requirements for inferencing in AI models? (Choose two)

Loading the model into memory

Pre-trained models

Extensive training data

Cloud-based inference only

NO.36 Which of the following describes the impact of generative AI in content creation?

Generative AI can create new content such as text, images, and videos.

Generative AI only processes pre-existing content.

Generative AI predicts customer preferences without creating new content.

Generative AI analyzes data without generating content.

NO.37 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?

NetApp SnapMirror

NetApp FlexClone

NetApp Snapshots

NetApp FabricPool

NO.38 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.

NO.39 A junior administrator is attempting to delete an old, unused Snapshot copy from a production volume to reclaim space but receives an error. The administrator is certain they are using the correct command and have the appropriate ‘vsadmin’ role.
The command and error are shown below:
cluster-1::> volume snapshot delete -vserver svm_prod -volume app_data -snapshot nightly.2025- 06-15_0015 Error: command failed: This operation is not permitted. Deletion of Snapshot copy “nightly.2025-
06-15_0015″ on volume “app_data” in Vserver “svm_prod” requires approval.
Use the “security multi-admin-verify approval show” command to view pending approvals.
What is the most likely reason the administrator cannot delete the Snapshot?

The Snapshot is locked by a SnapMirror relationship.

The volume is protected by SnapLock, and the Snapshot has not reached its expiry date.

Multi-Admin Verification (MAV) is enabled, and the destructive operation requires approval from another designated administrator.

The ‘vsadmin’ role does not have permission to delete Snapshots.

NO.40 An architect is designing the storage and network infrastructure for a new, large-scale AI cluster dedicated to training foundational models. The primary design goal is to achieve the highest possible data throughput and the lowest latency to ensure multi-million dollar GPU resources are never idle. Which two technologies are essential to include in the design to achieve this goal?
(Choose 2.)

A 10GbE Ethernet network for all data traffic.

NetApp StorageGRID as the primary storage for the training datasets.

An InfiniBand or RoCE-capable Ethernet network fabric.

GPUDirect Storage support on the storage system.

A tiered storage architecture using NetApp FabricPool.

NO.41 A data scientist is using the NetApp DataOps Toolkit for Python to automate the creation of a new, writable volume for an experiment. The script is intended to clone an existing dataset volume. When the script is executed, it fails with an error.
The relevant portion of the Python script is:
from netapp_dataops.k8s import clone_pvc
clone_pvc(
source_pvc_name=”dataset-v1-pvc”,
new_pvc_name=”experiment-clone-pvc”,
namespace=”ds-team-1″
)
The script produces the following error in the terminal:
‘Error: Failed to clone PVC. Source PVC ‘dataset-v1-pvc’ not found in namespace ‘ds-team-1′.’ What is the most likely cause of this error?

The NetApp DataOps Toolkit does not support cloning volumes.

The Kubernetes cluster does not have NetApp Trident installed.

The source PersistentVolumeClaim (PVC) named ‘dataset-v1-pvc’ does not exist or is in a different namespace.

The Python script is missing the necessary import statement for the toolkit.

NO.42 The data anonymization job, running in a Kubernetes pod, fails. The pod logs show a “Permission Denied” error when trying to access the source volume on the on-premises ASA. An administrator checks the export policy rule for the volume.
The active rule is as follows:
Rule_Index: 1
Client_Match: 10.50.0.0/16
Protocols: nfs4
Read_Only_Access: sys
Read_Write_Access: –
Superuser_Access: none
The Kubernetes pod that failed has an IP address of ‘10.60.5.10’.
What is the cause of the “Permission Denied” error?

The export policy only allows access via the NFSv4 protocol.

The pod’s IP address (‘10.60.5.10’) is not within the allowed client match range (‘10.50.0.0/16’).

The export policy does not grant read-write access, which is required by the anonymization job.

The ‘Superuser_Access’ setting is too restrictive.

NO.43 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?

The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.

Both the data scientist and the DevOps engineer should use Jupyter Notebooks.

The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).

Both the data scientist and the DevOps engineer should use automated production pipelines.

NO.44 What is the primary architectural advantage of using a NetApp AIPod with NVIDIA DGX servers for the AI training cluster, as described in the scenario?

It is a reference architecture that is pre-validated by NetApp and NVIDIA to eliminate design complexity and ensure predictable performance for AI workloads.

It exclusively uses object storage, which simplifies access for data scientists using S3-native tools.

It prioritizes CPU performance over GPU performance for traditional machine learning algorithms.

It is designed for small-scale, departmental AI projects and cannot be scaled.

NO.45 Which of the following applications use AI in the healthcare industry? (Choose two)

Predicting patient outcomes

Automating financial reporting

Diagnosing diseases

Managing hospital supply chains

NO.46 A data scientist needs to test a new data normalization technique. To do this, they require an isolated, writable copy of a 50 TB curated simulation dataset that resides on the NetApp ASA system. The operation must be completed as quickly as possible and consume minimal additional storage space. Which NetApp technology is the most appropriate solution for this requirement?

NetApp SnapMirror

NetApp FlexClone

NetApp XCP

NetApp FabricPool

NO.47 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?

It creates a full physical copy of the data, providing the best performance isolation.

It uses NetApp FlexClone technology to create a near-instantaneous, space-efficient writable copy of the volume, accelerating the experimental workflow.

It automatically migrates the data from the AFF A-Series to the StorageGRID system for the experiment.

It creates a read-only Snapshot, ensuring the integrity of the experimental data.