Network Appliance NetApp Certified AI Expert NS0-901
考試編碼: NS0-901
考試名稱: NetApp Certified AI Expert Exam
檢查更新時間: 2026-06-20
問題數量: 106 題
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1、不需要大量的時間金錢,僅需20-30個小時,自學成才,輕鬆通過Network Appliance NS0-901考試。
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4、在互聯網上提供24小時客戶服務。
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最新的 NetApp Certified AI Expert NS0-901 免費考試真題:
1. 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 NetApp StorageGRID to build the 5 PB cost-effective data lake.
B) Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.
C) Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
D) Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
E) Use a standard 10GbE network for all connectivity to reduce costs.
F) Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.
2. Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)
A) Google VertexAI
B) KubeFlow
C) TensorFlow Extended
D) RunAI
3. Which of the following techniques are used to maximize GPU utilization in AI workloads? (Choose two)
A) Using larger batch sizes
B) Utilizing CPU-based workloads for all tasks
C) Balancing compute and storage workloads efficiently
D) Scheduling GPU-intensive tasks during off-peak hours
4. 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 a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.
B) Configure QoS minimums on the training volumes to ensure they do not impact inference performance.
C) Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.
D) Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.
E) Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.
5. 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) Both the data scientist and the DevOps engineer should use automated production pipelines.
B) The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.
C) The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).
D) Both the data scientist and the DevOps engineer should use Jupyter Notebooks.
問題與答案:
| 問題 #1 答案: A,B,C,D | 問題 #2 答案: B,D | 問題 #3 答案: C,D | 問題 #4 答案: C,E | 問題 #5 答案: C |
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