[2026] Use Valid NS0-901 Exam - Actual Exam Question & Answer

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NEW QUESTION # 17
What is the primary reason for implementing a hybrid cloud architecture in this medical imaging scenario?

• A. To reduce the overall cost of storage by keeping all data in the public cloud.
• B. To simplify network management by using a single, global virtual network spanning the on- premises and cloud environments.
• C. 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.
• D. To exclusively use cloud-native AI services, eliminating the need for any on-premises hardware.

Answer: C

NEW QUESTION # 18
Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)

• A. TensorFlow Extended
• B. RunAI
• C. KubeFlow
• D. Google VertexAI

Answer: B,C

NEW QUESTION # 19
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. A custom "PII" category within BlueXP classification to identify specific medical record number formats.
• C. A manual review process where technicians visually inspect each scan for PII.
• D. NetApp SnapLock to make the source data immutable before scanning.
• E. NetApp BlueXP classification, configured to scan the on-premises ASA working environment.

Answer: B,E

NEW QUESTION # 20
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?

• A. 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.
• B. It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.
• C. It automatically terminates all low-priority jobs to free up the entire cluster.
• D. It sends an email notification to the administrator to manually reallocate the GPUs.

Answer: A

NEW QUESTION # 21
A new team of external auditors requires read-only access to the raw financial product documentation stored in the StorageGRID data lake. The data is in a bucket named 'prod-docs'.
The security team must ensure the auditors can list and read objects but cannot write, delete, or modify any data. Which is the most appropriate method to grant this specific access?

• A. Create a new StorageGRID tenant account for the auditors with full S3 access.
• B. Configure an NFS export policy on the 'prod-docs' bucket and provide the auditors with the mount path.
• C. Provide the auditors with the root access keys for the StorageGRID system.
• D. Create a new group and user within StorageGRID, and attach an S3 group policy that explicitly allows only 's3:GetObject' and 's3:ListBucket' actions on the 'prod-docs' bucket.

Answer: D

NEW QUESTION # 22
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?

• A. The on-premises ONTAP system is not configured for high-throughput.
• B. The model is too large to fit into the edge server's memory.
• C. Every inference request requires a high-latency round trip over the WAN to fetch data from the core data center.
• D. The edge server has insufficient CPU resources to run the model.

Answer: C

NEW QUESTION # 23
An architect is explaining different data storage paradigms to a new data engineering team. The team needs to understand where to store different types of data for their AI projects. Which statement accurately describes a key difference between a data lake and a data warehouse?

• A. A data lake stores structured, processed data for fast querying, while a data warehouse stores raw, unstructured data in its native format.
• B. A data lake is designed to store vast quantities of raw, multi-format data (structured, semi- structured, and unstructured), while a data warehouse is typically used for storing structured and filtered data that has already been processed for a specific purpose.
• C. A data lake is only for on-premises deployments, while a data warehouse is only for cloud deployments.
• D. A data lake uses a schema-on-write approach, while a data warehouse uses a schema-on-read approach.

Answer: B

NEW QUESTION # 24
In the data pipeline, which NetApp technology is used to create an efficient, block-level, incremental replication of the anonymized datasets from the on-premises ASA to the Cloud Volumes ONTAP instance in Azure?

• A. NetApp BlueXP copy and sync
• B. NetApp FabricPool
• C. NetApp XCP
• D. NetApp SnapMirror

Answer: D

NEW QUESTION # 25
An MLOps team is using the BlueXP Workload Factory to automate the deployment of a Jupyter notebook environment for a new data scientist. The deployment job fails. The team reviews the job details in the BlueXP UI.
Job ID: 78a1c-2b3d-4e5f
Service: BlueXP Workload Factory
Action: Deploy JupyterHub on Kubernetes
Status: FAILED
Error Message: "Failed to create PersistentVolumeClaim 'jupyter-pvc-user01'.
User 'service-account- bluexp' does not have permission to 'create' resource
'persistentvolumeclaims' in cluster 'k8s-prod- cluster-01'."
What is the most direct way to resolve this issue?

• A. Manually create the PersistentVolumeClaim using 'kubectl'.
• B. Modify the Kubernetes RBAC (Role-Based Access Control) configuration to grant the 'service- account-bluexp' the necessary permissions to create PVCs in the target cluster.
• C. Restart the BlueXP Connector to refresh its permissions.
• D. Increase the storage quota for the 'jupyter-pvc-user01' PVC.

Answer: B

NEW QUESTION # 26
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?

• A. Add more 100GbE network ports to the storage controllers.
• B. Upgrade the network switches to a model with a larger packet buffer.
• C. Isolate the storage traffic on a separate VLAN from the management traffic.
• D. Implement RDMA over Converged Ethernet (RoCE) and configure GPUDirect Storage.

Answer: D

NEW QUESTION # 27
An AI training job is running slower than expected. The infrastructure team suspects a compute bottleneck. The job involves processing high-resolution images with a complex convolutional neural network (CNN). They review the logs from the training script.
Epoch 1/100 - 3600s - loss: 1.253 - acc: 0.54
...
CPU_Utilization_During_Epoch: 99% (all cores)
GPU_0_Utilization_During_Epoch: 8%
GPU_1_Utilization_During_Epoch: 7%
...
Epoch 2/100 - 3610s - loss: 1.102 - acc: 0.61
What is the most likely cause of the poor training performance?

• A. The training script is CPU-bound, likely performing data augmentation or preprocessing on the CPU instead of offloading it to the GPUs.
• B. The network is saturated, preventing the GPUs from receiving data.
• C. The storage system cannot deliver data fast enough to the compute node.
• D. The model is too simple and does not effectively utilize the GPU's parallel processing capabilities.

Answer: A

NEW QUESTION # 28
The HPC cluster generates simulation data at an extremely high rate, requiring a storage system that can handle massively parallel writes from hundreds of compute nodes simultaneously. Which storage system and file protocol combination is the most appropriate choice for the HPC cluster's high-performance scratch space?

• A. A NetApp E-Series system serving a BeeGFS parallel file system.
• B. A Cloud Volumes ONTAP instance with a standard file system.
• C. A NetApp StorageGRID system accessed via the S3 protocol.
• D. A NetApp ASA system serving a single, large NFS volume.

Answer: A

NEW QUESTION # 29
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?

• A. Trident will automatically provision a 50 Gi volume on its default backend and bind it to the PVC.
• B. Trident will create a 1 Gi volume, as this is the default size for all PVCs.
• C. The PVC will remain in a "Pending" state until a PersistentVolume is manually created.
• D. The PVC will fail because a 'storageClassName' is not explicitly defined.

Answer: A

NEW QUESTION # 30
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. A standalone LLM deployed in an air-gapped environment with no access to the document repository.
• B. A predictive AI model trained to forecast market trends.
• C. A fine-tuning architecture where a base LLM is continuously retrained on the entire document repository.
• D. A Retrieval-Augmented Generation (RAG) architecture that retrieves relevant context from a local vector database to enrich prompts sent to the LLM.

Answer: D

NEW QUESTION # 31
The pod running the vector database on the Kubernetes cluster fails to start. An MLOps engineer runs 'kubectl describe pod vector-db-pod-0' and sees the following event message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 30s default-scheduler 0/8 nodes are available: 8 node(s) did not match pod anti-affinity rules.
The pod's manifest contains the following 'affinity' definition:
affinity:
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- labelSelector:
matchExpressions:
- key: app
operator: In
values:
- vector-db
topologyKey: "kubernetes.io/hostname"
What is the most likely reason the pod cannot be scheduled?

• A. The 'vector-db-pod-0' is requesting more GPU resources than are available on any node.
• B. The 'topologyKey' is invalid; it should be 'failure-domain.beta.kubernetes.io/zone'.
• C. The PersistentVolumeClaim for the pod is in a 'Pending' state.
• D. The pod's anti-affinity rule prevents it from being scheduled on any node that is already running another pod with the label 'app=vector-db'.

Answer: D

NEW QUESTION # 32
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?

• A. Use NetApp StorageGRID for the data lake and a NetApp ASA system for the vector database.
• B. Use NetApp Cloud Volumes ONTAP for the data lake and NetApp StorageGRID for the vector database.
• C. Use NetApp E-Series for the data lake and a NetApp ASA system for the vector database.
• D. Use a single, large NetApp ASA system for both the object data lake and the vector database.

Answer: A

NEW QUESTION # 33
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?

• A. Multi-Admin Verification (MAV)
• B. NetApp SnapLock (Compliance Mode)
• C. NetApp Volume Encryption (NVE)
• D. Autonomous Ransomware Protection (ARP)

Answer: D

NEW QUESTION # 34
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.)

• A. A private vector database hosted on an on-premises NetApp ONTAP system to store embeddings of the sensitive corporate data.
• B. A BlueXP Connector deployed in a public subnet with a public IP address to allow access to the LLM.
• C. A direct VPN connection from each data scientist's laptop to the public LLM provider.
• D. A policy in BlueXP classification to copy all sensitive data to a public cloud bucket for easier access.
• E. The BlueXP GenAI Toolkit, which acts as a proxy to intercept user prompts and enrich them with data from a local vector database.

Answer: A,E

NEW QUESTION # 35
The company's finance department mandates a cost-control strategy for the petabyte-scale StorageGRID data lake. The analysis shows that 80% of the simulation data is not accessed after
90 days but must be retained for five years for regulatory compliance. Which StorageGRID feature should the architect use to automatically reduce the long-term storage costs for this inactive data?

• A. Enable deduplication and compression on all StorageGRID storage nodes.
• B. Use NetApp SnapMirror to replicate the cold data to a different, lower-cost StorageGRID cluster.
• C. Configure an Information Lifecycle Management (ILM) policy that automatically moves objects older than 90 days to a lower-cost cloud archive tier, such as Amazon S3 Glacier Deep Archive.
• D. Implement a FabricPool policy to tier the data to a NetApp ASA system.

Answer: C

NEW QUESTION # 36
A distributed training job running on the AIPod fails to start. The MLOps engineer inspects the events for one of the pending training pods and sees the following message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 5m12s default-scheduler 0/4 nodes are available: 4 node(s) had no available volume zone.
The PersistentVolumeClaim (PVC) for this pod specifies a StorageClass that uses the 'ontap-nas' Trident provisioner.
he Trident logs show no errors.
What is the most likely cause of this scheduling failure?

• A. The 'requiredTopology' in the StorageClass is misconfigured, preventing Trident from finding a valid ONTAP backend in the same zone as the available compute nodes.
• B. The Kubernetes nodes do not have the necessary NFS client utilities installed.
• C. The NetApp ASA storage system is offline.
• D. The training pod's container image does not exist in the registry.

Answer: A

NEW QUESTION # 37
A national research laboratory is investing in a turnkey AI infrastructure solution. Their primary goal is to eliminate the complexity and risk of designing and integrating the compute, network, and storage components themselves. The solution must be pre-validated by the vendors to deliver predictable, linear performance as they scale from one to multiple compute nodes. Which two options represent this type of pre-validated, converged infrastructure solution for AI? (Choose two)

• A. A public cloud provider's general-purpose virtual machine instances.
• B. A single NetApp FAS system connected directly to a single server.
• C. A Cisco and NetApp FlexPod for AI solution.
• D. A custom-built server rack with components sourced from various different vendors.
• E. NetApp AIPod, which integrates NVIDIA DGX servers with NetApp storage and networking.

Answer: C,E

NEW QUESTION # 38
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. A tiered storage architecture using NetApp FabricPool.
• B. A 10GbE Ethernet network for all data traffic.
• C. GPUDirect Storage support on the storage system.
• D. NetApp StorageGRID as the primary storage for the training datasets.
• E. An InfiniBand or RoCE-capable Ethernet network fabric.

Answer: C,E

NEW QUESTION # 39
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