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Network Appliance NS0-901 Exam Overview:

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Network Appliance NetApp Certified AI Expert Sample Questions:

Question 1

What is the primary architectural benefit of using technologies like RDMA (Remote Direct Memory Access) and GPUDirect Storage in a high-performance AI training cluster?

A. They automatically encrypt data in-flight between the GPU servers and the storage system.
B. They allow GPUs to communicate directly with each other and with NVMe storage, bypassing the host CPU and system memory, which significantly reduces I/O latency and CPU overhead.
C. They enable the use of lower-cost Ethernet switches in place of InfiniBand fabrics without any performance degradation.
D. They reduce the power consumption of the storage array by offloading checksum calculations.

Question 2

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

Question 3

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

Question 4

An architect is designing a fully automated, end-to-end MLOps pipeline on Kubernetes for a computer vision use case. The pipeline must handle everything from data versioning to model deployment.
The required pipeline stages are:
1. Data Versioning: Create a new, immutable version of the master dataset for the pipeline run.
2. Data Preparation: Launch a pod to run a preprocessing script on the versioned data.
3. Model Training: Launch a distributed training job that reads the prepared data from a highperformance volume.
4. Model Deployment: Push the trained model to a production inference service.
Which combination of NetApp and Kubernetes technologies provides the most effective and automated solution for this entire pipeline?

A. Use NetApp SnapMirror for data versioning and manually create hostPath volumes for each pipeline stage.
B. Use NetApp XCP to copy the data for each stage and configure static PersistentVolumes for each pod.
C. Manually create a NetApp Snapshot via System Manager before each pipeline run, and use the NetApp DataOps Toolkit only for the training stage.
D. Use the NetApp DataOps Toolkit to create a Snapshot of the source data volume (for versioning), then create a FlexClone PVC from the snapshot for the preparation stage, and finally create a FlexGroup PVC for the training stage.
E. Use a single, large ReadWriteMany PVC for all stages to simplify the pipeline configuration.

Question 5

Which storage protocols are commonly used for handling large-scale AI data? (Choose two)

A. File-based systems
B. Object-based storage
C. Parallel file systems
D. POSIX-based file systems

Solutions: