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Network Appliance NetApp Certified AI Expert Sample Questions:

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 SnapMirror
B) NetApp BlueXP classification
C) NetApp XCP
D) NetApp FlexCache

2. A robotics company is developing a control system for an autonomous warehouse drone. The drone must learn to navigate complex environments to pick up packages. The development team has created a physics-based simulation where the drone can attempt the task millions of times.
The drone receives a positive reward for successfully retrieving a package and a negative penalty for collisions. Which type of machine learning algorithm is being used in this scenario?

A) Supervised learning
B) Unsupervised learning
C) Reinforcement learning
D) Generative learning

3. An architect is designing an AI solution for a European hospital chain to analyze patient diagnostic scans. The project is subject to strict GDPR regulations, which mandate that patient data cannot leave the sovereign territory. The application also requires near-instantaneous results for physicians reviewing the scans in the hospital.
Which deployment model best satisfies these security and performance requirements?

A) A centralized public cloud deployment in North America for maximum scalability.
B) A multi-cloud strategy using different providers for training and inference to avoid vendor lock-in.
C) An on-premises private cloud for training combined with edge deployments in each hospital for inference.
D) A hybrid model using a public cloud for training and on-premises for inference.

4. An AI architect is designing a solution for a legal firm. The primary goal is to allow lawyers to ask natural language questions about case law stored in a private, 50 TB document repository.
The key project constraints are as follows:
Project_Goal: Answer questions using proprietary, real-time legal documents.
Constraint_1: Must not alter the foundational LLM's weights due to compliance.
Constraint_2: Case law database is updated daily with new rulings.
Constraint_3: All generated answers must be traceable to a source document.
Which technology should the architect choose as the core of this solution?

A) A predictive AI model to classify legal documents.
B) A Retrieval-Augmented Generation (RAG) architecture.
C) A new LLM trained from scratch on the legal documents.
D) A fine-tuning pipeline to update the LLM daily.

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 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) A BlueXP Connector deployed in a public subnet with a public IP address to allow access to the LLM.
E) The BlueXP GenAI Toolkit, which acts as a proxy to intercept user prompts and enrich them with data from a local vector database.

Solutions: