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Network Appliance NetApp Certified AI Expert Sample Questions:

1. A data science team works primarily at a central data center but needs to run a short-term, burst- compute training job in the public cloud to take advantage of specialized GPUs that are not available on- premises. They need to efficiently and securely move a 20 TB dataset from their on- premises ONTAP cluster to a Cloud Volumes ONTAP instance for the duration of the job.
The data flow requirement is as follows:
Source: On-premises ONTAP cluster
Destination: Cloud Volumes ONTAP in AWS
Requirement: Efficient, secure, block-level data transfer.
Which NetApp technology is the most appropriate tool for this task?

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

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

3. The company's finance department mandates a cost-control strategy for the petabyte-scale StorageGRID data lake. The analysis shows that 80% of the simulation data is not accessed after
90 days but must be retained for five years for regulatory compliance. Which StorageGRID feature should the architect use to automatically reduce the long-term storage costs for this inactive data?

A) Implement a FabricPool policy to tier the data to a NetApp ASA system.
B) Enable deduplication and compression on all StorageGRID storage nodes.
C) Configure an Information Lifecycle Management (ILM) policy that automatically moves objects older than 90 days to a lower-cost cloud archive tier, such as Amazon S3 Glacier Deep Archive.
D) Use NetApp SnapMirror to replicate the cold data to a different, lower-cost StorageGRID cluster.

4. 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?

A) Retrieval-Augmented Generation (RAG), which fetches relevant information from the documents at query time to inform the model's response.
B) Predictive AI, which analyzes the documents to forecast future questions.
C) Fine-tuning, which retrains the model's last few layers on the new documents.
D) Unsupervised learning, which clusters the documents into topics for the model to browse.

5. 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 policy in BlueXP classification to copy all sensitive data to a public cloud bucket for easier access.
B) A private vector database hosted on an on-premises NetApp ONTAP system to store embeddings of the sensitive corporate data.
C) A BlueXP Connector deployed in a public subnet with a public IP address to allow access to the LLM.
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 direct VPN connection from each data scientist's laptop to the public LLM provider.

Solutions: