Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)

• A. TensorFlow Extended
• B. KubeFlow
• C. RunAI
• D. Google VertexAI

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 integrated development environments (IDEs) used exclusively for writing Python code.
• B. 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.
• C. They are networking protocols designed to accelerate data transfer between GPUs.
• D. They are specialized storage systems designed to hold large datasets for training.

Given the company's goal of combining physics-based simulations with AI-driven analytics on a shared data foundation, which industry trend does this project best represent?

• A. The replacement of all physical testing with digital simulations.
• B. The exclusive use of public cloud resources for all computational tasks.
• C. The separation of AI and HPC into dedicated, air-gapped environments.
• D. The convergence of AI, High-Performance Computing (HPC), and analytics on a unified data infrastructure.

An architect is designing a data pipeline for a predictive AI model that will forecast retail sales.
The pipeline must be robust, version-controlled, and efficient.
The proposed data flow is as follows:
1. Ingest: Raw sales data is copied daily from multiple point-of-sale (POS) systems to a central staging area on an on-premises ONTAP cluster.
2. Prepare: The raw data is messy. A data engineering team needs a clean, isolated, and writable copy of the latest daily data to perform cleansing and feature engineering tasks without impacting the original raw data.
3. Train: Once prepared, the cleansed dataset is used to retrain the predictive model on a GPU cluster.
This step must be repeatable with the exact same dataset for compliance.
4. Deploy: The newly trained model is pushed to production inference servers.
Which combination of NetApp technologies best supports this entire predictive AI lifecycle?
(Select all
that apply.)

• A. Use NetApp StorageGRID as the primary storage for the high-performance training stage.
• B. Use BlueXP backup and recovery to perform the initial data ingest from the POS systems.
• C. Use NetApp XCP to efficiently aggregate the raw sales data from POS systems into the central staging area.
• D. Use a RAG architecture for the sales forecasting model.
• E. Use NetApp FlexClone to create an instantaneous, space-efficient, writable copy of the daily raw data for the data preparation stage.
• F. Use NetApp Snapshots on the prepared dataset volume just before training to create an immutable, point-in-time version for compliance and reproducibility.

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 FlexClone to create full, writable copies of datasets for each experiment.
• 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. Use NetApp SnapMirror to replicate volumes from the AFF systems to StorageGRID for archival.
• F. Implement NetApp FabricPool to automatically tier cold data blocks from the AFF systems to StorageGRID.