Network Appliance NetApp Certified AI Expert Sample Questions:

1. An architect is designing a scalable, automated MLOps platform using Kubeflow on a Kubernetes cluster. The platform must support the entire AI lifecycle for multiple teams, with different storage requirements at each stage.
The key requirements are:
- Data Ingestion: A pipeline step needs a shared, read-write volume accessible by multiple pods to stage raw data.
- Experimentation: Data scientists need individual, isolated volumes for their Jupyter notebooks.
- Training: Distributed training jobs require a high-performance, parallel-access filesystem for reading training data.
- Automation: All storage must be provisioned automatically via Kubeflow pipeline definitions without manual intervention.
Which combination of technologies and configurations would create the most effective solution?

A) Use the NetApp DataOps Toolkit for all storage provisioning, bypassing Trident and Kubernetes PVCs.
B) Use the NetApp DataOps Toolkit for Python within the Kubeflow pipeline components to dynamically create and manage Trident PVCs for each stage.
C) Rely on hostPath volumes for all storage to ensure the highest performance.
D) Configure multiple Trident backends (e.g., 'ontap-nas' for standard volumes, 'ontap-nas-flexgroup' for parallel access) and corresponding StorageClasses.
E) Create a single, large NFS volume and mount it to all pods using a static PersistentVolume.

2. A financial services company is required by regulators to be able to trace any version of their deployed fraud detection model back to the exact dataset and source code commit used to train it.
The current MLOps workflow is as follows:
Code_Repository: Git (commit hash: a1b2c3d4)
Dataset_Location: /vol/prod_data/fraud_dataset_v3
Storage_System: NetApp ONTAP 9
Model_Output: /vol/models/fraud_model_v3.2
Which NetApp technology should be used to create an immutable, point-in-time, and space- efficient copy of the dataset that can be linked to the specific code commit and model version?

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

3. An architect is designing a comprehensive AI platform for a large enterprise. The platform must support the entire data lifecycle, from ingest at the edge to a central data lake, and finally to a high- performance training cluster.
The requirements are:
- Edge Ingest: Data must be collected at remote sites and efficiently replicated to the core.
- Data Lake: A central, petabyte-scale repository for unstructured data, accessible via the S3 protocol.
- Training Cluster: A high-performance compute cluster that requires low-latency, parallel file access to training datasets.
- Data Traceability: All datasets used for training must be immutably versioned.
Which combination of NetApp technologies and protocols should the architect choose to build this solution? (Select all that apply.)

A) Use NetApp ONTAP systems at the edge and NetApp SnapMirror to replicate data to the core data center.
B) Use a NetApp E-Series system with a parallel file system (like BeeGFS) to provide high- performance, parallel file access for the training cluster.
C) Use NetApp FlexCache to tier cold data from the data lake to the public cloud.
D) Use NetApp StorageGRID to create the petabyte-scale, S3-accessible data lake at the core.
E) Use iSCSI as the primary protocol for the data lake to ensure maximum compatibility.
F) Use NetApp Snapshots on the training dataset volumes to create immutable, point-in-time versions for traceability.

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

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

Solutions: