Network Appliance NS0-901 Certification All-in-One Exam Guide Jun-2026
Get Real NS0-901 Exam Dumps [Jun-2026] Practice Tests
NEW QUESTION # 61
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.)
- A. 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.
- B. Deploy NetApp StorageGRID on-premises to serve as a cost-effective capacity tier for the cold data.
- C. Deploy a single, large Cloud Volumes ONTAP instance to hold both the hot and cold data tiers.
- D. Use NetApp FabricPool to automatically and transparently tier cold data blocks from the AFF A- Series to the StorageGRID system.
- E. Deploy a NetApp AFF A-Series system on-premises to serve as the high-performance tier for the hot data.
- F. Use NetApp XCP to manually script the daily movement of cold files from the AFF system to StorageGRID.
Answer: B,D,E
NEW QUESTION # 62
The data science team in Azure reports that training jobs are taking longer than expected. An analysis of the Cloud Volumes ONTAP instance in Azure shows that the instance type is undersized for the I/O demands of the training workload. The architect needs to change the Azure VM instance type for the Cloud Volumes ONTAP system to a more powerful one.
The current configuration is:
Cloud_Provider: Azure
ONTAP_System: Cloud Volumes ONTAP (Single Node)
Current_Instance_Type: Standard_DS3_v2
Target_Instance_Type: Standard_E8s_v4
What is the most direct method to perform this operation using NetApp's management tools?
- A. Terminate the existing Cloud Volumes ONTAP instance and deploy a new one with the target instance type, then restore data from backup.
- B. From the BlueXP Canvas, select the Cloud Volumes ONTAP working environment and use the
"Change Instance Type" action in the Features panel. - C. SSH to the Cloud Volumes ONTAP instance and run an ONTAP command to modify the underlying VM.
- D. Use the Azure portal to manually change the VM instance type.
Answer: B
NEW QUESTION # 63
An AI platform team is investigating poor I/O performance for a specific workload that involves processing hundreds of thousands of small metadata files. The application is running on a Kubernetes cluster with storage provided by a NetApp ONTAP system over NFS. Performance metrics show acceptable network throughput but very high latency for metadata operations (e.g., open, stat, close).
The current storage configuration is as follows:
Storage_System: NetApp AFF A-Series
Protocol: NFSv4.1
Workload_Profile: Metadata-intensive, many small file lookups
Observed_Issue: High latency on metadata operations, slow job completion Which storage architecture would be better suited to handle this specific metadata-intensive workload?
- A. A NetApp Cloud Volumes ONTAP instance in a different region.
- B. A NetApp E-Series system running a parallel file system like BeeGFS.
- C. A NetApp SnapLock-enabled volume for data protection.
- D. A NetApp StorageGRID object storage system.
Answer: B
NEW QUESTION # 64
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?
- A. Create a single, large NFS volume and mount it to all pods using a static PersistentVolume.
- B. Rely on hostPath volumes for all storage to ensure the highest performance.
- C. Use the NetApp DataOps Toolkit for all storage provisioning, bypassing Trident and Kubernetes PVCs.
- D. Use the NetApp DataOps Toolkit for Python within the Kubeflow pipeline components to dynamically create and manage Trident PVCs for each stage.
- E. Configure multiple Trident backends (e.g., 'ontap-nas' for standard volumes, 'ontap-nas-flexgroup' for parallel access) and corresponding StorageClasses.
Answer: D,E
NEW QUESTION # 65
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 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.
- B. Create a new StorageGRID tenant account for the auditors with full S3 access.
- C. Configure an NFS export policy on the 'prod-docs' bucket and provide the auditors with the mount path.
- D. Provide the auditors with the root access keys for the StorageGRID system.
Answer: A
NEW QUESTION # 66
Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)
- A. TensorFlow Extended
- B. RunAI
- C. Google VertexAI
- D. KubeFlow
Answer: B,D
NEW QUESTION # 67
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. NetApp AIPod, which integrates NVIDIA DGX servers with NetApp storage and networking.
- B. A Cisco and NetApp FlexPod for AI solution.
- C. A custom-built server rack with components sourced from various different vendors.
- D. A single NetApp FAS system connected directly to a single server.
- E. A public cloud provider's general-purpose virtual machine instances.
Answer: A,B
NEW QUESTION # 68
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?
- A. It prioritizes CPU performance over GPU performance for traditional machine learning algorithms.
- B. It is designed for small-scale, departmental AI projects and cannot be scaled.
- C. It is a reference architecture that is pre-validated by NetApp and NVIDIA to eliminate design complexity and ensure predictable performance for AI workloads.
- D. It exclusively uses object storage, which simplifies access for data scientists using S3-native tools.
Answer: C
NEW QUESTION # 69
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 # 70
An AI research team is experiencing slow model training times. Their performance monitoring indicates that the GPUs are frequently idle, waiting for data. They want to implement a single technology change to create a more direct data path between their storage and GPUs.
Their current setup is as follows:
Compute: Server with NVIDIA A100 GPUs
Storage: NetApp AFF A-Series (All-Flash)
Network: 100GbE Ethernet
Data_Path: Storage -> Host CPU/Memory -> GPU Memory
Which technology should the architect recommend to specifically address this data path inefficiency?
- A. A faster CPU in the server
- B. NetApp FabricPool
- C. GPUDirect Storage
- D. NetApp SnapMirror
Answer: C
NEW QUESTION # 71
A network administrator, attempting to harden the security of the data center, modifies a firewall access control list (ACL). Immediately afterward, the "Advisor Assistant" application pods can no longer mount their required NFS volumes from the AFF A-Series. The MLOps team confirms the pods are stuck in a 'ContainerCreating' state with a 'MountVolume.SetUp failed... connection timed out' error. The administrator provides the new, active firewall rule:
RULE | ACTION | PROTOCOL | SOURCE_IP_RANGE | DEST_IP_RANGE | DEST_PORT --|--|-
|--||--51 | ALLOW | TCP | 10.20.5.0/24 | 10.20.10.0/24 | 2049
What is the most likely reason for the mount failures?
- A. The firewall rule is blocking the Portmapper/RPCbind service (TCP/UDP port 111), which is necessary for the initial NFS mount negotiation.
- B. The firewall rule is blocking the NFSv4 protocol, which requires TCP port 2050.
- C. The 'SOURCE_IP_RANGE' is incorrect and does not include the Kubernetes pod IP addresses.
- D. The firewall rule is blocking the NFS lock manager (NLM) protocol, which is required for file locking.
Answer: A
NEW QUESTION # 72
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.)
- A. Replace the NFS protocol with iSCSI for all training data access.
- B. Use NetApp FlexCache to create a local cache of the training data on each compute node.
- C. Implement a NetApp FlexGroup volume to spread the dataset across multiple constituent volumes and aggregates.
- D. Enable QoS maximums on the training volume to limit its IOPS.
- E. Increase the number of network ports connected to the storage controller.
Answer: B,C
NEW QUESTION # 73
A data science team reports that their Jupyter notebook pod, which was previously working, is now failing to start. The pod's status is 'CrashLoopBackOff'. An MLOps engineer investigates and finds that the pod's PersistentVolumeClaim (PVC) is bound, but the pod logs show a "Permission denied" error when trying to write to its '/data' mount point.
The engineer checks the Trident backend configuration associated with the pod's StorageClass:
apiVersion: trident.netapp.io/v1
kind: TridentBackendConfig
metadata:
name: ontap-nas-eco
spec:
version: 1
storageDriverName: ontap-nas
managementLIF: 10.10.20.5
dataLIF: 10.10.20.10
svm: svm-prod-ds
exportPolicy: read-only-policy
What is the most likely cause of the "Permission denied" error?
- A. The 'storageDriverName' should be 'ontap-san' for all AI workloads.
- B. The Trident backend is configured to use an export policy ('read-only-policy') that does not grant write permissions to the Kubernetes nodes.
- C. The 'dataLIF' is configured incorrectly and is unreachable from the Kubernetes nodes.
- D. The Kubernetes pod has an invalid 'securityContext' that prevents it from writing to any volume.
Answer: B
NEW QUESTION # 74
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?
- A. The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.
- B. Both the data scientist and the DevOps engineer should use Jupyter Notebooks.
- C. The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).
- D. Both the data scientist and the DevOps engineer should use automated production pipelines.
Answer: C
NEW QUESTION # 75
An organization recently suffered a ransomware attack that encrypted several volumes on their primary ONTAP storage system, including a critical volume containing curated training data. The security team needs to implement a solution that can proactively detect and block ransomware- like file I/O patterns and automatically create a secure Snapshot copy before any damage is done.
The current ONTAP configuration is as follows:
ONTAP_Version: 9.12.1
Security_Features: SnapLock (Compliance Mode) on archive volumes
Anti-Virus_Scan: Enabled (Vscan)
Ransomware_Detection: Not configured
Which ONTAP feature should be enabled to provide this proactive, automated protection?
- A. Increase the frequency of scheduled Snapshots.
- B. Enable Autonomous Ransomware Protection (ARP).
- C. Enable Multi-Admin Verification (MAV) on all volumes.
- D. Use BlueXP backup and recovery to back up the volumes to the cloud more frequently.
Answer: B
NEW QUESTION # 76
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 Volume Encryption (NVE)
- C. Autonomous Ransomware Protection (ARP)
- D. NetApp SnapLock (Compliance Mode)
Answer: C
NEW QUESTION # 77
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?
- A. Modify the NFS export policy on the 'vol_kb' volume on the on-premises ONTAP cluster to grant access to the BlueXP Connector.
- B. Check the IAM permissions for the role associated with the S3 bucket.
- C. Re-run the vector database embedding job.
- D. Fine-tune the LLM with the latest data instead of using the RAG system.
Answer: A
NEW QUESTION # 78
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?
- A. Implement a converged data infrastructure where both the HPC and AI clusters access a single, high- performance data lake built on NetApp storage.
- B. Use NetApp XCP to perform the data copy, as it is faster than a standard NFS copy.
- C. Install faster CPUs in the AI cluster's storage controllers.
- D. Upgrade the network connection between the two storage systems to 200GbE.
Answer: A
NEW QUESTION # 79
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. Deploy a single, global NetApp StorageGRID across all three locations to act as a unified data plane.
- B. 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. - C. Use Cloud Volumes ONTAP at the edge, NetApp StorageGRID at the core, and on-premises ONTAP for cloud archive.
- D. 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.
Answer: B
NEW QUESTION # 80
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 SnapMirror
- C. NetApp FabricPool
- D. NetApp XCP
Answer: B
NEW QUESTION # 81
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