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Network Appliance NetApp Certified AI Expert Sample Questions:

1. An AI infrastructure architect is tasked with designing a solution to address two critical challenges in a large, multi-petabyte AI environment:
1. Cost: A significant portion of the data on the high-performance all-flash storage is inactive but must remain online. The cost of storing this cold data on the performance tier is prohibitive.
2. Traceability: Data scientists need a simple, space-efficient way to version their datasets at key points in their workflow to ensure reproducibility.
The environment consists of NetApp AFF A-Series and NetApp StorageGRID systems.
Which combination of NetApp technologies should the architect implement to solve both challenges simultaneously? (Select all that apply.)

A) Train data scientists to use NetApp Snapshots to create point-in-time, read-only versions of their data volumes.
B) Implement NetApp FabricPool to automatically tier cold data blocks from the AFF systems to StorageGRID.
C) Use NetApp XCP to periodically move cold data from the AFF systems to StorageGRID.
D) Use BlueXP backup and recovery to create backups on StorageGRID, then delete the original volumes from the AFF systems.
E) Implement NetApp FlexClone to create full, writable copies of datasets for each experiment.
F) Use NetApp SnapMirror to replicate volumes from the AFF systems to StorageGRID for archival.

2. An architect is designing the storage and network infrastructure for a new, large-scale AI cluster dedicated to training foundational models. The primary design goal is to achieve the highest possible data throughput and the lowest latency to ensure multi-million dollar GPU resources are never idle. Which two technologies are essential to include in the design to achieve this goal?
(Choose 2.)

A) NetApp StorageGRID as the primary storage for the training datasets.
B) GPUDirect Storage support on the storage system.
C) A 10GbE Ethernet network for all data traffic.
D) A tiered storage architecture using NetApp FabricPool.
E) An InfiniBand or RoCE-capable Ethernet network fabric.

3. An MLOps team uses a variety of platforms to manage their AI workloads. They need to understand the primary function of each tool within their ecosystem. Which statement best describes the role of an MLOps/LLMOps platform like Kubeflow or Run:AI?

A) They are orchestration and management platforms that automate and streamline the entire AI/ML lifecycle, from data preparation and model training to deployment and monitoring.
B) They are integrated development environments (IDEs) used exclusively for writing Python code.
C) They are specialized storage systems designed to hold large datasets for training.
D) They are networking protocols designed to accelerate data transfer between GPUs.

4. The firm's data science team needs to run a high-priority, interactive model analysis job that requires immediate access to two GPUs. However, all GPUs in the cluster are currently allocated to long-running, lower-priority batch training jobs.
The MLOps platform, Run:AI, shows the following queue status:
JOB_ID | PROJECT | STATUS | PRIORITY | GPU_ALLOCATED
||--|-|
batch_job_1 | team_a | Running | Low | 2
batch_job_2 | team_a | Running | Low | 2
batch_job_3 | team_b | Running | Low | 4
interactive_1| team_c | Pending | High | 2 (requested)
How does the Run:AI platform address this resource contention to allow the high-priority job to run?

A) It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.
B) It sends an email notification to the administrator to manually reallocate the GPUs.
C) It automatically terminates all low-priority jobs to free up the entire cluster.
D) It automatically pauses one of the low-priority jobs, saves its state, and allocates its GPUs to the high- priority job, placing the paused job back in the queue.

5. What is the primary role of NetApp Trident in a Kubernetes environment designed for AI workloads?

A) To provide a web-based IDE, like Jupyter, for data scientists to develop models.
B) To act as a dynamic storage orchestrator, provisioning persistent storage from NetApp backends on- demand for containerized applications.
C) To function as a container runtime interface for executing AI models.
D) To directly accelerate GPU computations using specialized drivers.

Solutions: