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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

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

2. 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) NFS is incapable of storing image or video files.
B) The S3 API cannot be used to access data stored on an NFS file system.
C) A NAS filer cannot be deployed on-premises.
D) A traditional scale-up NAS system will face scalability and cost-effectiveness challenges at the petabyte scale compared to an object storage system.

3. 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) Propose a hybrid approach where a base model is first lightly fine-tuned for persona, then used within a RAG system for factual grounding.
C) Prioritize fine-tuning to embed the company's brand voice and persona into the model.
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.

4. 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) Both the data scientist and the DevOps engineer should use Jupyter Notebooks.
B) The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).
C) Both the data scientist and the DevOps engineer should use automated production pipelines.
D) The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.

5. 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) Install faster CPUs in the AI cluster's storage controllers.
B) Use NetApp XCP to perform the data copy, as it is faster than a standard NFS copy.
C) Upgrade the network connection between the two storage systems to 200GbE.
D) Implement a converged data infrastructure where both the HPC and AI clusters access a single, high- performance data lake built on NetApp storage.

Solutions: