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NS0-901 Practice Test Questions Updated 106 Questions

NEW QUESTION 33
An AI architect is designing a solution for a legal firm. The primary goal is to allow lawyers to ask natural language questions about case law stored in a private, 50 TB document repository.
The key project constraints are as follows:
Project_Goal: Answer questions using proprietary, real-time legal documents.
Constraint_1: Must not alter the foundational LLM’s weights due to compliance.
Constraint_2: Case law database is updated daily with new rulings.
Constraint_3: All generated answers must be traceable to a source document.
Which technology should the architect choose as the core of this solution?

A fine-tuning pipeline to update the LLM daily.

A Retrieval-Augmented Generation (RAG) architecture.

A predictive AI model to classify legal documents.

A new LLM trained from scratch on the legal documents.

NEW QUESTION 34
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.

NEW QUESTION 35
Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)

KubeFlow

TensorFlow Extended

Google VertexAI

RunAI

NEW QUESTION 36
An online retail company’s recommendation engine, which provides real-time product suggestions to users, is experiencing unacceptable latency. The inference application is running on a correctly-sized edge server, but user requests are taking over 500ms to process. An architect reviews the data access pattern and infrastructure diagram.
Application_Location: Edge Server (In-store)
Data_Source_Location: Core Data Center (On-premises ONTAP)
Data_Required_for_Inference: User profile data, product catalog vectors Network_Path: Edge -> WAN -> Core Data Center Observed_Latency: 550ms What is the most likely cause of the high inference latency?

The edge server has insufficient CPU resources to run the model.

The on-premises ONTAP system is not configured for high-throughput.

The model is too large to fit into the edge server’s memory.

Every inference request requires a high-latency round trip over the WAN to fetch data from the core data center.

NEW QUESTION 37
The firm’s CFO is concerned about the rising costs of the on-premises AI infrastructure. A storage utilization report shows that of the 200 TB of data on the high-performance AFF A-Series, 150 TB consists of inactive, older versions of product documents that are rarely accessed but must be kept online for regulatory reasons.
The current storage landscape is:
– Performance Tier: NetApp AFF A-Series (200 TB used)
– Capacity Tier: NetApp StorageGRID (1.5 PB used)
What is the most effective and automated solution to reduce the storage cost of the performance tier without impacting data accessibility?

Manually identify and delete the 150 TB of inactive data from the AFF A-Series.

Implement NetApp FabricPool to automatically tier the inactive data blocks from the AFF A-Series to the StorageGRID system.

Purchase an additional, larger AFF A-Series system to gain better storage efficiency through deduplication.

Use NetApp SnapMirror to replicate the entire 200 TB volume to the StorageGRID system and then delete the source.

NEW QUESTION 38
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.

NEW QUESTION 39
The AI training jobs on the AIPod are performing below expectations. The NVIDIA DGX servers’ GPUs show low utilization. A performance analysis reveals that the bottleneck is not the storage system itself, but the network path between the storage and the compute nodes.
The current network configuration is as follows:
Network_Fabric: 100GbE Standard Ethernet
Protocol: NFS over TCP/IP
Data_Path: Storage -> Host CPU -> GPU Memory
Which network architecture enhancement would provide the most significant performance improvement by reducing latency and CPU overhead?

Implement RDMA over Converged Ethernet (RoCE) and configure GPUDirect Storage.

Upgrade the network switches to a model with a larger packet buffer.

Add more 100GbE network ports to the storage controllers.

Isolate the storage traffic on a separate VLAN from the management traffic.

NEW QUESTION 40
An architect is designing a scalable, automated MLOps platform using Kubeflow on a Kubernetes cluster. The platform must support the entire AI lifecycle for multiple teams, with different storage requirements at each stage.
The key requirements are:
– Data Ingestion: A pipeline step needs a shared, read-write volume accessible by multiple pods to stage raw data.
– Experimentation: Data scientists need individual, isolated volumes for their Jupyter notebooks.
– Training: Distributed training jobs require a high-performance, parallel-access filesystem for reading training data.
– Automation: All storage must be provisioned automatically via Kubeflow pipeline definitions without manual intervention.
Which combination of technologies and configurations would create the most effective solution?

Use the NetApp DataOps Toolkit for all storage provisioning, bypassing Trident and Kubernetes PVCs.

Create a single, large NFS volume and mount it to all pods using a static PersistentVolume.

Configure multiple Trident backends (e.g., ‘ontap-nas’ for standard volumes, ‘ontap-nas-flexgroup’ for parallel access) and corresponding StorageClasses.

Use the NetApp DataOps Toolkit for Python within the Kubeflow pipeline components to dynamically create and manage Trident PVCs for each stage.

Rely on hostPath volumes for all storage to ensure the highest performance.

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.

NEW QUESTION 42
The Chief Information Security Officer (CISO) is concerned about the risk of a ransomware attack encrypting the critical vector database hosted on the NetApp AFF A-Series. The CISO wants a solution that can proactively detect and block a live attack in real-time, not just recover from a backup after the fact.
Which NetApp security feature should the architect enable on the vector database volume to meet this requirement?

NetApp SnapLock (Compliance Mode)

Multi-Admin Verification (MAV)

NetApp Volume Encryption (NVE)

Autonomous Ransomware Protection (ARP)

NEW QUESTION 43
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.

NEW QUESTION 44
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?

Fine-tuning, which retrains the model’s last few layers on the new documents.

Retrieval-Augmented Generation (RAG), which fetches relevant information from the documents at query time to inform the model’s response.

Predictive AI, which analyzes the documents to forecast future questions.

Unsupervised learning, which clusters the documents into topics for the model to browse.

NEW QUESTION 45
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.

NEW QUESTION 46
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

NEW QUESTION 47
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.

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Latest [Aug 31, 2026] Network Appliance NS0-901 Real Exam Dumps PDF [Q33-Q47]