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Network Appliance NetApp Certified AI Expert Sample Questions:

1. An organization wants to provide its data science team with a secure, on-demand method for using a powerful generative AI model with their private, sensitive corporate data. The solution must ensure that the private data is never exposed to the public internet or the public LLM API endpoint.
The architect is designing a solution using BlueXP.
Which two components are essential for building this secure solution? (Choose 2.)

A) A direct VPN connection from each data scientist's laptop to the public LLM provider.
B) A private vector database hosted on an on-premises NetApp ONTAP system to store embeddings of the sensitive corporate data.
C) A policy in BlueXP classification to copy all sensitive data to a public cloud bucket for easier access.
D) The BlueXP GenAI Toolkit, which acts as a proxy to intercept user prompts and enrich them with data from a local vector database.
E) A BlueXP Connector deployed in a public subnet with a public IP address to allow access to the LLM.

2. A data scientist needs to test a new data normalization technique. To do this, they require an isolated, writable copy of a 50 TB curated simulation dataset that resides on the NetApp ASA system. The operation must be completed as quickly as possible and consume minimal additional storage space. Which NetApp technology is the most appropriate solution for this requirement?

A) NetApp FlexClone
B) NetApp XCP
C) NetApp SnapMirror
D) NetApp FabricPool

3. 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?

A) NetApp FlexClone
B) NetApp SnapMirror
C) NetApp FabricPool
D) NetApp Snapshots

4. 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 StorageGRID
B) NetApp XCP
C) NetApp FlexCache
D) NetApp SnapMirror
E) NetApp FabricPool

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

Solutions: