Great NS0-901 Exam Dumps (V8.02) with Real Questions and Answers: Help You Prepare for the NetApp Certified AI Expert Exam Well

1. An organization is developing a new AI-powered application. The initial phase involves feeding a curated 50 TB dataset of labeled images into a complex neural network, allowing the model to learn and adjust its internal parameters over millions of iterations. The second phase involves deploying this finalized model to a web service where it will process single, user-uploaded images and return a classification in real-time.

Which statement accurately describes these two phases?

2. An AI architect is planning the resource allocation for a new project. The primary task is to process millions of unlabeled customer reviews to identify naturally occurring groups or themes without any prior guidance.

The project requirements are summarized below:

Task: Discover

hidden patterns in text data

Input_Data: 10

million unlabeled text reviews

Output:

Clustered groups of related reviews

Supervision:

None

Which type of machine learning algorithm is required for this task?

3. A financial services company has deployed a real-time fraud detection model at the edge. The model is designed for low-latency inference. However, monitoring reports indicate that the infrastructure costs are excessively high, and GPU utilization is consistently low. The architect reviews the deployment configuration.

Instance_Type: NVIDIA

DGX A100 (8 GPUs)

Storage_Tier:

High-Performance All-Flash (NetApp ASA)

Network: 100GbE

RoCE

GPU_Utilization_Avg:

5%

Monthly_Cost:

$15,000

Workload_Profile:

Low-volume, sporadic, real-time predictions

What is the most likely cause of the high costs and low utilization?

4. 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.)

5. 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?

6. 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?

7. 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?

8. 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?

9. 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?

10. An enterprise is planning a generative AI solution to power its internal support chatbot. The architect must choose between a RAG-based approach and fine-tuning a base model. The project stakeholders have provided a list of prioritized requirements.

| Requirement | Priority | Details |

| | -- | |

| Factual Accuracy | Critical | Must use the latest product documentation, updated daily. |

| Brand Voice & Persona | High | Must respond in the company's specific, formal tone. |

| Development Cost | High | Limited budget for GPU compute hours for model training. |

| Data Traceability | Critical | Must be able to cite the exact source document for each answer. |

Which two recommendations should the architect make to best satisfy these requirements? (Choose 2.)

11. 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?

12. An AI architect needs to design a complete, end-to-end data pipeline for a new generative AI application at a financial services firm. The application will allow internal analysts to query a massive, 500 TB archive of historical market data and reports to generate summaries.

The firm has the following environment and requirements:

Data_Sources: A mix

of on-premises ONTAP filers and StorageGRID S3 buckets.

Requirement_1: All

queries must be answered using only the private data archive.

Requirement_2: All

generated summaries must provide citations to the source reports.

Requirement_3: All

data containing client PII must be identified and excluded from the LLM

context.

Requirement_4: The

solution must be cost-effective for the large, mostly-read data archive.

Which set of actions and technologies constitutes the most robust and compliant solution? (Select all that apply.)

13. What is the primary role of NetApp Trident in a Kubernetes environment designed for AI workloads?

14. 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?

15. An MLOps engineer is deploying a training pod that requires a high-performance volume. After applying the pod and PVC manifests, the pod remains in a `Pending` state. The engineer runs `kubectl describe pod training-pod-7d8c` and sees the following event:

Events:

Type

Reason

Age

From

Message

-

-

-

-

Warning FailedScheduling 2m15s default-scheduler 0/4

nodes are available: 1 node(s) had volume node affinity conflict, 3 node(s)

didn't find available persistent volume to bind.

The engineer then inspects the associated PVC and sees its status is also `Pending`.

What is the most likely cause of this issue?

16. An organization's AI platform team needs to provide two distinct tiers of storage for their data scientists on a single Kubernetes cluster:

1. `gold-tier`: Extremely low-latency storage for active model training, using an all-flash NetApp ASA system.

2. `bronze-tier`: Cost-effective, high-capacity storage for data staging and archiving, using a NetApp StorageGRID system.

How should the platform team configure NetApp Trident to meet these requirements? (Select all that apply.)

17. 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?

18. 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?

19. What is the primary architectural benefit of using technologies like RDMA (Remote Direct Memory Access) and GPUDirect Storage in a high-performance AI training cluster?

20. 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?

21. An AI infrastructure engineer is troubleshooting a poorly performing distributed training job. The job is running across multiple nodes, each equipped with powerful GPUs. The engineer observes that overall GPU utilization is unexpectedly low. System-level monitoring on the compute nodes provides the following metrics during a training run.

avg_gpu_utilization:

25%

avg_cpu_iowait_percent: 65%

avg_network_bandwidth_util: 95% (on a 10GbE network)

storage_array_latency: <1ms

Given these metrics, what is the most likely bottleneck causing the low GPU utilization?

22. 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.)

23. 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?

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 cost-effective 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.)

25. An AI architect is designing a storage solution for a new training cluster. The primary workload consists of training large language models, which involves sequential reads of massive datasets. The key requirement is to maximize GPU utilization by providing the highest possible data throughput. Cost is a secondary concern to performance.

Which NetApp storage system is the most appropriate choice for this workload?

26. 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?

27. An organization has a core data center with a large AI training cluster and several remote edge locations for data ingest and local inference. The edge locations frequently need access to the latest models trained in the core data center, but WAN bandwidth is limited and can be unreliable. Users at the edge are reporting slow model loading times.

An architect reviews the data access logs from an edge site:

Timestamp:

2025-07-11T15:30:00Z

Event:

Model_Load_Request

Model_Path:

nfs://core-filer.example.com/vol/models/latest_model.pkl

Source_IP:

192.168.100.15 (Edge Server)

Destination_IP: 10.1.1.50

(Core Filer)

Status: SUCCESS

Duration: 3600s (60

minutes)

What is the most likely cause of the slow model loading times at the edge?

28. A company is running its AI training workloads on a NetApp AFF A-Series system. To manage costs, they want to automatically move inactive training datasets and older model checkpoints from the high-performance all-flash tier to a lower-cost object storage tier, such as an on-premises StorageGRID or a public cloud bucket. The process must be transparent to the data scientists and not require changes to their scripts or file paths.

Which two NetApp technologies should be combined to achieve this goal? (Choose 2.)

29. 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?

30. An AI infrastructure architect is tasked with designing a solution to address two critical challenges in a large, multi-petabyte AI environment:

1. Cost: A significant portion of the data on the high-performance all-flash storage is inactive but must remain online. The cost of storing this cold data on the performance tier is prohibitive.

2. Traceability: Data scientists need a simple, space-efficient way to version their datasets at key points in their workflow to ensure reproducibility.

The environment consists of NetApp AFF A-Series and NetApp StorageGRID systems.

Which combination of NetApp technologies should the architect implement to solve both challenges simultaneously? (Select all that apply.)

31. An architect is designing an AI solution for a European hospital chain to analyze patient diagnostic scans. The project is subject to strict GDPR regulations, which mandate that patient data cannot leave the sovereign territory. The application also requires near-instantaneous results for physicians reviewing the scans in the hospital.

Which deployment model best satisfies these security and performance requirements?

32. 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?

33. 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?

34. A pharmaceutical company is creating a "digital twin" of its manufacturing process. This involves running complex simulations (an HPC workload) that generate massive datasets.

The company wants to use this data immediately for two other purposes:

1. Analytics: Business analysts need to run complex queries on the simulation output using tools like Spark.

2. AI Training: Data scientists need to use the same output as a training set for a predictive maintenance model.

The company wants to avoid creating separate data silos for each workload.

Which two NetApp technologies are best suited for building a unified data lake that can efficiently serve all three workloads (HPC, Analytics, AI)? (Choose 2.)

35. A research lab uses a fleet of autonomous drones to collect high-resolution aerial imagery for agricultural analysis. The drones land at a remote edge location and offload their data. The AI models for image analysis are trained at a central data center. The team is using NetApp SnapMirror to replicate the data from the edge to the core. However, the data scientists are complaining that the datasets arriving at the data center are often incomplete or corrupted.

An administrator reviews the SnapMirror configuration and status via the BlueXP API:

{

"source": { "workingEnvironmentId":

"OnPrem-Edge-Filer-1", "volumeName":

"drone_data_raw" },

"destination": { "workingEnvironmentId":

"Core-Datacenter-A800", "volumeName":

"drone_data_replicated" },

"mirrorState": "broken-off",

"relationshipStatus": "idle",

"unhealthyReason": "Transfer failed. Destination volume is out

of space.",

"lastTransferInfo": {

"transferError": "No space left on device"

}

}

What is the direct cause of the incomplete datasets at the data center?

36. 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?

37. An AI team is embarking on a project to train a new, large-scale computer vision model from scratch. The lead architect emphasizes that the success of the project depends on four fundamental inputs that must be available and managed throughout the training process.

Which of the following are the four essential requirements for model generation?

38. A healthcare organization plans to use a large dataset of patient records to train a predictive model. Before training, they must identify and segregate all records containing Personally Identifiable Information (PII) to comply with privacy regulations. The data resides on an on-premises NetApp ONTAP cluster. The organization needs an automated tool to scan the data in-place and tag files containing PII without moving the data.

The project requirements are as follows:

Task: Identify PII in

a large dataset.

Data_Location:

On-premises ONTAP cluster.

Constraint: Data must

not be moved from its source location for scanning.

Output: Tagged files

containing PII.

Which NetApp tool is designed for this specific task?

39. 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?

40. 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 2.)