Network Appliance NetApp Certified AI Expert : NS0-901

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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

1. 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.)

A) Prioritize a RAG architecture to meet the critical requirements for factual accuracy and data traceability.
B) Prioritize fine-tuning to embed the company's brand voice and persona into the model.
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) Use RAG exclusively, as prompt engineering alone can fully replicate a specific brand voice and persona.
E) Recommend training a new LLM from scratch to ensure both brand voice and factual accuracy are built-in.

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

3. Which of the following are typical requirements for inferencing in AI models? (Choose two)

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

4. 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) 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.
C) Both projects are examples of fine-tuning and have similar infrastructure needs.
D) Project A is a model building task and requires a significantly larger and more powerful compute and storage infrastructure than Project B.
E) Both projects should use NetApp StorageGRID as the primary storage for training to ensure low latency.

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D) The NFS version used between the core and edge is outdated.

Solutions: