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Nutanix Certified Professional - Artificial Intelligence NCP-AI 6.10

Last Update 19 hours ago Total Questions : 75

The Nutanix Certified Professional - Artificial Intelligence NCP-AI 6.10 content is now fully updated, with all current exam questions added 19 hours ago. Deciding to include NCP-AI practice exam questions in your study plan goes far beyond basic test preparation.

You'll find that our NCP-AI exam questions frequently feature detailed scenarios and practical problem-solving exercises that directly mirror industry challenges. Engaging with these NCP-AI sample sets allows you to effectively manage your time and pace yourself, giving you the ability to finish any Nutanix Certified Professional - Artificial Intelligence NCP-AI 6.10 practice test comfortably within the allotted time.

Question # 21

An administrator notices increased model inference latency and frequent timeout errors in a Nutanix AI deployment during peak usage.

What is the most effective action to troubleshoot and resolve the performance issue?

A.

Reduce the number of API keys to limit external access to the model.

B.

Restart the Nutanix Kubernetes cluster to clear cached memory and reset service states.

C.

Disable logging and monitoring tools to free up system resources.

D.

Analyze resource metrics and scale out the model service to handle increased load.

Question # 22

An administrator is monitoring the performance of a deployed Large Language Model within the Nutanix Enterprise AI platform. After initial deployment, users report slow inference response times and occasional timeouts when accessing the model through its API endpoint.

The administrator reviews the performance metrics available in the NAI Dashboard and notes the following:

    CPU usage is consistently high across all inference-serving containers.

    Memory utilization is nearing the allocated limits for the model service.

    The request latency graph shows increasing average inference times during peak usage.

Which action should the administrator take to improve performance and reduce latency?

A.

Restart the model container to clear memory cache and allow the system to rebalance performance.

B.

Scale out the number of instances and allocate additional CPU and memory resources.

C.

Disable logging temporarily to reduce resource consumption during peak load periods.

D.

Increase the number of API keys assigned to the endpoint to allow more concurrent access.

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