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Network Appliance NetApp Certified AI Expert 認定 NS0-901 試験問題:

1. An architect is designing a global infrastructure for a company that develops AI for autonomous vehicles.
The design must accommodate three distinct locations and functions:
1. Edge (Test Tracks): Fleets of test cars generate 100s of TBs of sensor data per day. This data must be ingested locally with high performance.
2. Core (Primary Data Center): The raw data from all edge sites must be aggregated here. This location houses the primary data lake and the main GPU cluster for large-scale model training.
3. Cloud (Public Cloud Provider): Data scientists want to use cloud-native tools for experimental data processing and model development. They also need a cost-effective location for long-term archiving of raw data.
Which combination of deployment locations and NetApp technologies creates the most logical and efficient end-to-end solution?

A) Use Cloud Volumes ONTAP at the edge, NetApp StorageGRID at the core, and on-premises ONTAP for cloud archive.
B) Use NetApp E-Series at the edge, NetApp ASA at the core, and NetApp StorageGRID in the cloud.
Use SnapMirror to move data between all three tiers.
C) Use NetApp ONTAP systems at the edge and core, and Cloud Volumes ONTAP in the public cloud.
Use SnapMirror to replicate data from edge to core, and FabricPool to tier data from the core to the cloud.
D) Deploy a single, global NetApp StorageGRID across all three locations to act as a unified data plane.

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

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

3. An AI infrastructure engineer is troubleshooting a poorly performing distributed training job. The job is running across multiple nodes, each equipped with powerful GPUs. The engineer observes that overall GPU utilization is unexpectedly low. System-level monitoring on the compute nodes provides the following metrics during a training run.
avg_gpu_utilization: 25%
avg_cpu_iowait_percent: 65%
avg_network_bandwidth_util: 95% (on a 10GbE network)
storage_array_latency: <1ms
Given these metrics, what is the most likely bottleneck causing the low GPU utilization?

A) The CPU is underpowered and cannot preprocess the data fast enough for the GPUs.
B) The storage array is too slow and cannot serve data quickly enough.
C) The network connecting the compute nodes and storage is saturated and has become the primary bottleneck.
D) The GPUs are faulty and cannot process data at their rated speed.

4. A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
Which tools are best suited for each of these roles?

A) The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.
B) Both the data scientist and the DevOps engineer should use Jupyter Notebooks.
C) Both the data scientist and the DevOps engineer should use automated production pipelines.
D) The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).

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

質問と回答：