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

1. An automotive company runs crash simulations on a dedicated High-Performance Computing (HPC) cluster and trains computer vision models on a separate AI cluster. Data scientists are complaining about the long delays required to move terabytes of simulation output data from the HPC storage to the AI cluster's storage before they can begin training.
The current data flow is as follows:
HPC Cluster -> --Manual Copy (NFS)--> -> AI Cluster
An architect has been asked to redesign the infrastructure to eliminate this data movement bottleneck.
Which architectural change would be most effective?

A) Install faster CPUs in the AI cluster's storage controllers.
B) Upgrade the network connection between the two storage systems to 200GbE.
C) Use NetApp XCP to perform the data copy, as it is faster than a standard NFS copy.
D) Implement a converged data infrastructure where both the HPC and AI clusters access a single, high- performance data lake built on NetApp storage.

2. Advisors report that some queries to the chatbot are unacceptably slow, taking several seconds to respond. The MLOps team isolates the issue to the RAG retrieval step. Performance monitoring of the NetApp AFF A-Series hosting the vector database shows the following metrics during periods of high query load.
avg_read_latency: 3500 microseconds (3.5 ms)
avg_write_latency: 400 microseconds (0.4 ms)
iops_total: 15,000
cpu_utilization_storage_node: 15%
workload_profile: 95% small, random reads
Given these metrics, what is the most likely performance bottleneck?

A) The AFF A-Series system is underpowered and cannot handle the IOPS load.
B) The network between the application and the storage system is saturated.
C) The vector database is poorly indexed, causing inefficient, slow lookups that translate to high read latency on the storage.
D) The storage node CPU is the bottleneck, preventing faster data processing.

3. A financial services company has deployed a real-time fraud detection model at the edge. The model is designed for low-latency inference. However, monitoring reports indicate that the infrastructure costs are excessively high, and GPU utilization is consistently low. The architect reviews the deployment configuration.
Instance_Type: NVIDIA DGX A100 (8 GPUs)
Storage_Tier: High-Performance All-Flash (NetApp ASA)
Network: 100GbE RoCE
GPU_Utilization_Avg: 5%
Monthly_Cost: $15,000
Workload_Profile: Low-volume, sporadic, real-time predictions
What is the most likely cause of the high costs and low utilization?

A) The compute and storage infrastructure is sized for a large-scale training workload, not a lightweight inference workload.
B) The network latency is too high for an edge deployment.
C) The model was trained using supervised learning, which is inefficient for fraud detection.
D) The storage tier is too slow, causing the GPUs to wait for data.

4. Which of the following techniques are used to maximize GPU utilization in AI workloads? (Choose two)

A) Utilizing CPU-based workloads for all tasks
B) Using larger batch sizes
C) Scheduling GPU-intensive tasks during off-peak hours
D) Balancing compute and storage workloads efficiently

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

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