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Network Appliance NetApp Certified AI Expert Sample Questions:

1. An AI team is embarking on a project to train a new, large-scale computer vision model from scratch. The lead architect emphasizes that the success of the project depends on four fundamental inputs that must be available and managed throughout the training process. Which of the following are the four essential requirements for model generation?

A) A project manager, a data scientist, a software engineer, and a budget.
B) A data lake, a data warehouse, a data pipeline, and a data mart.
C) Data, code, compute, and time.
D) A pre-trained model, a validation set, an inference engine, and a cloud provider.

2. An organization's AI platform team needs to provide two distinct tiers of storage for their data scientists on a single Kubernetes cluster:
1. 'gold-tier': Extremely low-latency storage for active model training, using an all-flash NetApp ASA system.
2. 'bronze-tier': Cost-effective, high-capacity storage for data staging and archiving, using a NetApp StorageGRID system.
How should the platform team configure NetApp Trident to meet these requirements? (Select all that apply.)

A) Create a single StorageClass named 'multi-tier-storage' that references both backends.
B) Create a single Trident backend that points to both the ASA and StorageGRID systems simultaneously.
C) Configure the 'bronze-tier' StorageClass to reference the StorageGRID backend.
D) Create two separate Trident backend configurations, one for the ASA ('backend-asa.yaml') and one for StorageGRID ('backend-sgrid.yaml').
E) Configure the 'gold-tier' StorageClass to reference the ASA backend.
F) Create two distinct StorageClasses, 'gold-tier' and 'bronze-tier'.

3. An organization is developing a new AI-powered application. The initial phase involves feeding a curated 50 TB dataset of labeled images into a complex neural network, allowing the model to learn and adjust its internal parameters over millions of iterations. The second phase involves deploying this finalized model to a web service where it will process single, user-uploaded images and return a classification in real-time.
Which statement accurately describes these two phases?

A) Both Phase 1 and Phase 2 are examples of training.
B) Phase 1 is training, and Phase 2 is inferencing.
C) Both Phase 1 and Phase 2 are examples of inferencing.
D) Phase 1 is inferencing, and Phase 2 is training.

4. An architect is designing a global infrastructure for a company that develops AI for autonomous vehicles.
The design must accommodate three distinct locations and functions:
1. Edge (Test Tracks): Fleets of test cars generate 100s of TBs of sensor data per day. This data must be ingested locally with high performance.
2. Core (Primary Data Center): The raw data from all edge sites must be aggregated here. This location houses the primary data lake and the main GPU cluster for large-scale model training.
3. Cloud (Public Cloud Provider): Data scientists want to use cloud-native tools for experimental data processing and model development. They also need a cost-effective location for long-term archiving of raw data.
Which combination of deployment locations and NetApp technologies creates the most logical and efficient end-to-end solution?

A) Use NetApp E-Series at the edge, NetApp ASA at the core, and NetApp StorageGRID in the cloud.
Use SnapMirror to move data between all three tiers.
B) Deploy a single, global NetApp StorageGRID across all three locations to act as a unified data plane.
C) Use NetApp ONTAP systems at the edge and core, and Cloud Volumes ONTAP in the public cloud.
Use SnapMirror to replicate data from edge to core, and FabricPool to tier data from the core to the cloud.
D) Use Cloud Volumes ONTAP at the edge, NetApp StorageGRID at the core, and on-premises ONTAP for cloud archive.

5. The firm has acquired a competitor, and the volume of proprietary documents for the "Advisor Assistant" is expected to triple, exceeding the capacity of the current StorageGRID data lake. The architect needs to expand the data lake's capacity non-disruptively.
The current StorageGRID status is:
System_Health: Nominal
Node_Count: 6 (3 Storage Nodes, 3 Admin/Gateway Nodes)
Usable_Capacity: 1.5 PB
Used_Capacity: 1.4 PB (93%)
What is the standard procedure for scaling the capacity of the on-premises NetApp StorageGRID system?

A) Add new storage nodes to the existing StorageGRID system and execute an expansion procedure.
B) Use FabricPool to tier the excess data from StorageGRID to a public cloud provider.
C) Replace the disks in the existing storage nodes with higher-capacity drives.
D) Provision a new, separate StorageGRID system and use BlueXP copy and sync to move the data.

Solutions: