Network Appliance NetApp Certified AI Expert - NS0-901 Exam Questions [2026]

QUESTION NO: 1
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?

A. NetApp BlueXP classification B. NetApp XCP C. NetApp SnapMirror D. NetApp FlexCache

Correct Answer: A

QUESTION NO: 2
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?

A. The edge server does not have enough RAM to cache the model effectively. B. The core ONTAP filer is using slow, capacity-based disks. C. The NFS version used between the core and edge is outdated. D. The model file is being transferred over a slow, high-latency WAN link for every load request.

Correct Answer: D

QUESTION NO: 3
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.)

A. NetApp XCP B. NetApp SnapMirror C. NetApp StorageGRID D. NetApp FabricPool E. NetApp FlexCache

Correct Answer: C,D

QUESTION NO: 4
Which of the following are typical requirements for inferencing in AI models? (Choose two)

A. Extensive training data B. Cloud-based inference only C. Pre-trained models D. Loading the model into memory

Correct Answer: C,D

QUESTION NO: 5
An MLOps team uses a variety of platforms to manage their AI workloads. They need to understand the primary function of each tool within their ecosystem. Which statement best describes the role of an MLOps/LLMOps platform like Kubeflow or Run:AI?

A. They are specialized storage systems designed to hold large datasets for training. B. They are orchestration and management platforms that automate and streamline the entire AI/ML lifecycle, from data preparation and model training to deployment and monitoring. C. They are networking protocols designed to accelerate data transfer between GPUs. D. They are integrated development environments (IDEs) used exclusively for writing Python code.

Correct Answer: B

QUESTION NO: 6
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.)

A. Use BlueXP classification to perform an initial scan of the portfolio database to identify and tag all columns containing PII. B. Rely on a disclaimer in the chatbot UI telling advisors not to enter queries related to specific clients. C. Move the entire client portfolio database to an encrypted, air-gapped system. D. Fine-tune the LLM on a sanitized dataset to teach it to ignore PII. E. 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.

Correct Answer: A,E

QUESTION NO: 7
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 custom-built server rack with components sourced from various different vendors. B. A public cloud provider's general-purpose virtual machine instances. C. A Cisco and NetApp FlexPod for AI solution. D. NetApp AIPod, which integrates NVIDIA DGX servers with NetApp storage and networking. E. A single NetApp FAS system connected directly to a single server.

Correct Answer: C,D

QUESTION NO: 8
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. Every inference request requires a high-latency round trip over the WAN to fetch data from the core data center. B. The edge server has insufficient CPU resources to run the model. C. The on-premises ONTAP system is not configured for high-throughput. D. The model is too large to fit into the edge server's memory.

Correct Answer: A

A. Prioritize fine-tuning to embed the company's brand voice and persona into the model. B. Prioritize a RAG architecture to meet the critical requirements for factual accuracy and data traceability. C. Propose a hybrid approach where a base model is first lightly fine-tuned for persona, then used within a RAG system for factual grounding. D. Recommend training a new LLM from scratch to ensure both brand voice and factual accuracy are built-in. E. Use RAG exclusively, as prompt engineering alone can fully replicate a specific brand voice and persona.

Correct Answer: B,C

QUESTION NO: 10
The data pipeline that updates the vector database from the StorageGRID data lake fails. An MLOps engineer uses the BlueXP API to check the status of the copy and sync job responsible for moving new documents to a staging area for processing.
The API returns the following JSON response:
{
"id": "rel_5f8g7h2k",
"volumeName": "product_docs_source",
"svmName": "svm_datalake",
"relationshipStatus": "FAILED",
"s3Location": "s3://vector-staging-bucket/latest/",
"progress": 87,
"failureMessage": "Sync failed.
Target credentials invalid. Check permissions for data broker 'db-gcp-1' on target bucket 'vector- staging-bucket'."
}
Based on this output, what is the most direct cause of the pipeline failure?

A. The BlueXP data broker service account or role lacks the necessary IAM permissions to write to the target S3 bucket. B. The source StorageGRID system is offline. C. The vector database embedding process has crashed. D. The network connection between the data broker and the StorageGRID system is blocked by a firewall.

Correct Answer: A