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 Snapshots on the prepared dataset volume just before training to create an immutable, point-in-time version for compliance and reproducibility.
• B. Use NetApp StorageGRID as the primary storage for the high-performance training stage.
• 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 BlueXP backup and recovery to perform the initial data ingest from the POS systems.
• F. Use NetApp FlexClone to create an instantaneous, space-efficient, writable copy of the daily raw data for the data preparation stage.

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

The HPC cluster generates simulation data at an extremely high rate, requiring a storage system that can handle massively parallel writes from hundreds of compute nodes simultaneously. Which storage system and file protocol combination is the most appropriate choice for the HPC cluster's high-performance scratch space?

• A. A Cloud Volumes ONTAP instance with a standard file system.
• B. A NetApp ASA system serving a single, large NFS volume.
• C. A NetApp StorageGRID system accessed via the S3 protocol.
• D. A NetApp E-Series system serving a BeeGFS parallel file system.

An AI platform team is investigating poor I/O performance for a specific workload that involves processing hundreds of thousands of small metadata files. The application is running on a Kubernetes cluster with storage provided by a NetApp ONTAP system over NFS. Performance metrics show acceptable network throughput but very high latency for metadata operations (e.g., open, stat, close).
The current storage configuration is as follows:
Storage_System: NetApp AFF A-Series
Protocol: NFSv4.1
Workload_Profile: Metadata-intensive, many small file lookups
Observed_Issue: High latency on metadata operations, slow job completion Which storage architecture would be better suited to handle this specific metadata-intensive workload?

• A. A NetApp E-Series system running a parallel file system like BeeGFS.
• B. A NetApp StorageGRID object storage system.
• C. A NetApp SnapLock-enabled volume for data protection.
• D. A NetApp Cloud Volumes ONTAP instance in a different region.

A research institute is designing an infrastructure to support its entire AI drug discovery pipeline.
The pipeline has two distinct workload requirements:
1. Training: A team of data scientists needs to train several large transformer models simultaneously using a 500 TB dataset of genomic sequences. This process requires maximum data throughput to keep the GPUs saturated.
2. Inference: Once trained, the models are deployed to an internal web portal where researchers submit individual protein sequences for analysis. These queries must return results with the lowest possible latency.
Which infrastructure design best satisfies both requirements? (Choose 2.)

• A. Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.
• B. Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.
• C. Configure QoS minimums on the training volumes to ensure they do not impact inference performance.
• D. Use a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.
• E. Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.