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Network Appliance NetApp Certified AI Expert Sample Questions:

1. A university is building a shared AI research platform. They have two primary requirements:
1. Performance: A "hot" research area for active model training and development that requires the absolute lowest latency and highest throughput to support multiple, simultaneous GPU- intensive jobs.
The data in this area is around 50 TB.
2. Capacity & Cost: A "cold" data lake to store over 5 PB of raw, unstructured experimental data that is infrequently accessed but must be retained for compliance and future use. This tier must be as costeffective as possible.
Which combination of NetApp hardware and technologies should an architect select to build a complete, optimized, and cost-effective solution? (Select all that apply.)

A) Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
B) Use a standard 10GbE network for all connectivity to reduce costs.
C) Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
D) Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.
E) Use NetApp StorageGRID to build the 5 PB cost-effective data lake.
F) Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.

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

3. Which of the following platforms provides tools for model training and deployment specifically for AI workloads?

A) Domino Data Labs
B) All of the above
C) RunAI
D) Google VertexAI

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

5. A research institute is designing an infrastructure to support its entire AI drug discovery pipeline.
The pipeline has two distinct workload requirements:
1. Training: A team of data scientists needs to train several large transformer models simultaneously using a 500 TB dataset of genomic sequences. This process requires maximum data throughput to keep the GPUs saturated.
2. Inference: Once trained, the models are deployed to an internal web portal where researchers submit individual protein sequences for analysis. These queries must return results with the lowest possible latency.
Which infrastructure design best satisfies both requirements? (Choose 2.)

A) Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.
B) Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.
C) Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.
D) Configure QoS minimums on the training volumes to ensure they do not impact inference performance.
E) Use a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.

Solutions: