[2026] Pass Key features of NS0-901 Course with Updated 106 Questions
NS0-901 Sample Practice Exam Questions 2026 Updated Verified
NEW QUESTION # 17
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 Recently, users have complained that the answers provided by the chatbot are outdated and do not reflect the latest product updates.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Based on the log, what is the most likely cause of the outdated answers?
- A. The process that converts staged documents into vectors and updates the vector database is not running.
- B. The BlueXP copy and sync service is failing to copy data to the staging area.
- C. The LLM needs to be fine-tuned with the new product information.
- D. The knowledge base itself has not been updated with the latest product information.
Answer: A
NEW QUESTION # 18
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. A fine-tuning pipeline to update the LLM daily.
- B. A Retrieval-Augmented Generation (RAG) architecture.
- C. A new LLM trained from scratch on the legal documents.
- D. A predictive AI model to classify legal documents.
Answer: B
NEW QUESTION # 19
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 # 20
A research institute is designing an infrastructure to support its entire AI drug discovery pipeline.
The pipeline has two distinct workload requirements:
1. Training: A team of data scientists needs to train several large transformer models simultaneously using a 500 TB dataset of genomic sequences. This process requires maximum data throughput to keep the GPUs saturated.
2. Inference: Once trained, the models are deployed to an internal web portal where researchers submit individual protein sequences for analysis. These queries must return results with the lowest possible latency.
Which infrastructure design best satisfies both requirements? (Choose 2.)
- A. Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.
- B. Use a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.
- C. Configure QoS minimums on the training volumes to ensure they do not impact inference performance.
- D. Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.
- E. Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.
Answer: A,E
NEW QUESTION # 21
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 # 22
An AI team is planning two separate projects. The architect needs to provision the appropriate infrastructure for each.
| | Project A | Project B|
| -- | | - |
| Goal | Build a novel image recognition model from scratch.
| Adapt an existing, pre- trained LLM to understand company-specific jargon. |
| Input Data | 10 million new, unlabeled images. | A 50 GB text corpus of internal documents. |
| Required Compute | Very High (Weeks of multi-GPU training) | Moderate (Hours of single-GPU training) | Which two statements accurately describe the infrastructure requirements for these projects?
(Choose two.)
- A. Project A requires only a large amount of data, while Project B requires only a large amount of code.
- B. Both projects are examples of fine-tuning and have similar infrastructure needs.
- C. Both projects should use NetApp StorageGRID as the primary storage for training to ensure low latency.
- D. 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.
- E. Project A is a model building task and requires a significantly larger and more powerful compute and storage infrastructure than Project B.
Answer: D,E
NEW QUESTION # 23
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. NetApp FabricPool
- B. A faster CPU in the server
- C. GPUDirect Storage
- D. NetApp SnapMirror
Answer: C
NEW QUESTION # 24
A university is building a shared AI research platform. They have two primary requirements:
1. Performance: A "hot" research area for active model training and development that requires the absolute lowest latency and highest throughput to support multiple, simultaneous GPU- intensive jobs.
The data in this area is around 50 TB.
2. Capacity & Cost: A "cold" data lake to store over 5 PB of raw, unstructured experimental data that is infrequently accessed but must be retained for compliance and future use. This tier must be as costeffective as possible.
Which combination of NetApp hardware and technologies should an architect select to build a complete, optimized, and cost-effective solution? (Select all that apply.)
- A. Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
- B. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.
- C. Use a standard 10GbE network for all connectivity to reduce costs.
- D. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.
- E. Use NetApp StorageGRID to build the 5 PB cost-effective data lake.
- F. Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
Answer: A,D,E,F
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
An architect is designing a fully automated, end-to-end MLOps pipeline on Kubernetes for a computer vision use case. The pipeline must handle everything from data versioning to model deployment.
The required pipeline stages are:
1. Data Versioning: Create a new, immutable version of the master dataset for the pipeline run.
2. Data Preparation: Launch a pod to run a preprocessing script on the versioned data.
3. Model Training: Launch a distributed training job that reads the prepared data from a highperformance volume.
4. Model Deployment: Push the trained model to a production inference service.
Which combination of NetApp and Kubernetes technologies provides the most effective and automated solution for this entire pipeline?
- A. Use the NetApp DataOps Toolkit to create a Snapshot of the source data volume (for versioning), then create a FlexClone PVC from the snapshot for the preparation stage, and finally create a FlexGroup PVC for the training stage.
- B. Use NetApp XCP to copy the data for each stage and configure static PersistentVolumes for each pod.
- C. Use a single, large ReadWriteMany PVC for all stages to simplify the pipeline configuration.
- D. Use NetApp SnapMirror for data versioning and manually create hostPath volumes for each pipeline stage.
- E. Manually create a NetApp Snapshot via System Manager before each pipeline run, and use the NetApp DataOps Toolkit only for the training stage.
Answer: A
NEW QUESTION # 27
The firm has acquired a competitor, and the volume of proprietary documents for the "Advisor Assistant" is expected to triple, exceeding the capacity of the current StorageGRID data lake. The architect needs to expand the data lake's capacity non-disruptively.
The current StorageGRID status is:
System_Health: Nominal
Node_Count: 6 (3 Storage Nodes, 3 Admin/Gateway Nodes)
Usable_Capacity: 1.5 PB
Used_Capacity: 1.4 PB (93%)
What is the standard procedure for scaling the capacity of the on-premises NetApp StorageGRID system?
- A. Add new storage nodes to the existing StorageGRID system and execute an expansion procedure.
- B. Replace the disks in the existing storage nodes with higher-capacity drives.
- C. Provision a new, separate StorageGRID system and use BlueXP copy and sync to move the data.
- D. Use FabricPool to tier the excess data from StorageGRID to a public cloud provider.
Answer: A
NEW QUESTION # 28
The architect needs to design an efficient data flow to move curated training sets from the central StorageGRID data lake to the high-performance NetApp ASA system used by the AI cluster. The process must be manageable from a single interface and should be automatable. Which two NetApp technologies should be used to implement this data pipeline stage? (Choose 2.)
- A. A manual copy process using an S3 client on a bastion host.
- B. NetApp BlueXP copy and sync service to create and manage the data synchronization relationship between the S3 source and the NFS/CIFS destination.
- C. NetApp FabricPool to automatically tier the data from StorageGRID to the ASA.
- D. A custom Python script using the NetApp DataOps Toolkit to trigger the data movement.
- E. NetApp SnapMirror to replicate data from the StorageGRID object store to the ASA's file system.
Answer: B,D
NEW QUESTION # 29
A robotics company is developing a control system for an autonomous warehouse drone. The drone must learn to navigate complex environments to pick up packages. The development team has created a physics-based simulation where the drone can attempt the task millions of times.
The drone receives a positive reward for successfully retrieving a package and a negative penalty for collisions. Which type of machine learning algorithm is being used in this scenario?
- A. Reinforcement learning
- B. Supervised learning
- C. Generative learning
- D. Unsupervised learning
Answer: A
NEW QUESTION # 30
Which of the following applications use AI in the healthcare industry? (Choose two)
- A. Automating financial reporting
- B. Predicting patient outcomes
- C. Managing hospital supply chains
- D. Diagnosing diseases
Answer: B,D
NEW QUESTION # 31
A data science team works primarily at a central data center but needs to run a short-term, burst- compute training job in the public cloud to take advantage of specialized GPUs that are not available on- premises. They need to efficiently and securely move a 20 TB dataset from their on- premises ONTAP cluster to a Cloud Volumes ONTAP instance for the duration of the job.
The data flow requirement is as follows:
Source: On-premises ONTAP cluster
Destination: Cloud Volumes ONTAP in AWS
Requirement: Efficient, secure, block-level data transfer.
Which NetApp technology is the most appropriate tool for this task?
- A. NetApp FlexCache
- B. NetApp FabricPool
- C. NetApp XCP
- D. NetApp SnapMirror
Answer: D
NEW QUESTION # 32
Which storage protocols are commonly used for handling large-scale AI data? (Choose two)
- A. POSIX-based file systems
- B. Parallel file systems
- C. File-based systems
- D. Object-based storage
Answer: B,D
NEW QUESTION # 33
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?
- A. The pod's IP address ('10.60.5.10') is not within the allowed client match range ('10.50.0.0/16').
- B. The export policy does not grant read-write access, which is required by the anonymization job.
- C. The 'Superuser_Access' setting is too restrictive.
- D. The export policy only allows access via the NFSv4 protocol.
Answer: A
NEW QUESTION # 34
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?
- A. It creates a full physical copy of the data, providing the best performance isolation.
- B. It uses NetApp FlexClone technology to create a near-instantaneous, space-efficient writable copy of the volume, accelerating the experimental workflow.
- C. It creates a read-only Snapshot, ensuring the integrity of the experimental data.
- D. It automatically migrates the data from the AFF A-Series to the StorageGRID system for the experiment.
Answer: B
NEW QUESTION # 35
An AI architect is reviewing the design for a new data lake. The primary requirement is to store petabytes of unstructured data (images, video, sensor logs) in a highly durable, scalable, and cost- effective manner. The data will be accessed via S3 API by various data processing and analytics applications.
The initial design proposes using a traditional Network Attached Storage (NAS) filer with a large number of disks. The architect reviews the proposal:
Proposed_System: Traditional NAS Filer
Protocol: NFSv4
Scalability_Model: Scale-up
Metadata_Handling: Centralized in filer head
Cost_per_GB: Moderate
Why is this proposed system a poor choice for a petabyte-scale data lake?
- A. The S3 API cannot be used to access data stored on an NFS file system.
- B. A NAS filer cannot be deployed on-premises.
- C. NFS is incapable of storing image or video files.
- D. A traditional scale-up NAS system will face scalability and cost-effectiveness challenges at the petabyte scale compared to an object storage system.
Answer: D
NEW QUESTION # 36
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