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Network Appliance NetApp Certified AI Expert Sample Questions:

1. An internal audit requires the firm to prove the exact state of the financial product documentation that was used by the chatbot to answer a specific query from last Tuesday. The query log shows the request was processed at '2025-07-11T14:30:00Z'. The data pipeline that updates the vector database runs daily at midnight.
Which NetApp technology allows the team to instantly access an immutable, point-in-time version of the vector database volume as it existed on that specific day?

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

2. The architect is designing the complete, automated data pipeline from the on-premises data center to the Azure cloud for this medical imaging project. The design must prioritize security, efficiency, and reproducibility.
Which sequence of actions provides the most robust and automated solution?

A) Manually copy raw data to Azure, run a cloud-based script to anonymize it, then start training.
B) Use FabricPool to automatically tier all raw data to Azure Blob storage, then mount the blob container to the training VMs.
C) Use BlueXP classification to scan the on-prem data; use a DataOps Toolkit script to trigger a FlexClone of the source volume for anonymization; use SnapMirror to replicate the anonymized clone to Cloud Volumes ONTAP; then begin training in Azure.
D) Use SnapMirror to replicate the raw, sensitive data to Azure first, then run BlueXP classification in the cloud to find PII.

3. 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) 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.
B) Both projects are examples of fine-tuning and have similar infrastructure needs.
C) Project A requires only a large amount of data, while Project B requires only a large amount of code.
D) Both projects should use NetApp StorageGRID as the primary storage for training to ensure low latency.
E) Project A is a model building task and requires a significantly larger and more powerful compute and storage infrastructure than Project B.

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

5. Which of the following are typical requirements for inferencing in AI models? (Choose two)

A) Loading the model into memory
B) Cloud-based inference only
C) Extensive training data
D) Pre-trained models

Solutions: