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Nutanix NCP-AI Exam Syllabus Topics:

Topic	Details
Topic 1	Deploy a Nutanix Enterprise AI Environment: This domain covers initial deployment tasks including validating prerequisites, installing Enterprise AI components, and configuring DNS, URLs, and certificates for secure platform operation.
Topic 2	Connect Applications to a Nutanix Enterprise AI Environment: This domain focuses on configuring and validating application connections to endpoints and monitoring endpoint metrics to track application usage and integration performance.
Topic 3	Troubleshoot a Nutanix Enterprise AI Environment: This domain covers diagnosing and resolving performance issues, resource utilization problems, cluster health check failures, and model import or endpoint creation errors.
Topic 4	Perform Day 2 Operations: This domain addresses ongoing management including preparing application integration requirements, interpreting and optimizing performance metrics, monitoring access for anomalies, and selecting appropriate LLMs for optimal output.
Topic 5	Configure a Nutanix Enterprise AI Environment: This domain focuses on operational setup including onboarding users, importing large language models, creating and securing API endpoints with keys, and delivering these endpoints to consumers.

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Nutanix Certified Professional - Artificial Intelligence v6.10 Sample Questions (Q32-Q37):

NEW QUESTION # 32
Immediately after installing NAI, an administrator must verify that every pod in the namespace nai-system is in a ready state using the command kubectl get pods -n nai-system.
Which ready value would indicate a pod is highly available and healthy?

A. 1/1
B. 2/2
C. 1/2
D. 2/1

Answer: B

NEW QUESTION # 33
What should an administrator configure with a fully qualified domain name (FQDN) on the DNS domain that is accessible to a Kubernetes cluster?

A. Kserve
B. Kube-api
C. Knative-serving
D. Istio

Answer: D

NEW QUESTION # 34
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. Increase the number of API keys assigned to the endpoint to allow more concurrent access.
C. Disable logging temporarily to reduce resource consumption during peak load periods.
D. Scale out the number of instances and allocate additional CPU and memory resources.

Answer: D

NEW QUESTION # 35
What is the minimum number of supported GPUs required to deploy Google pre-validated LLMs?

A. 0
B. 1
C. 2
D. 3

Answer: A

NEW QUESTION # 36
An AI/ML Admin initiates an import of a new Large Language Model (LLM) from Hugging Face using its Model URL into Nutanix Enterprise AI. After the import process begins, the LLM's status on the Models page changes to Pending.
Based on this status, what is the most probable cause for the delay?

A. The Hugging Face access token provided is unauthorizeD.
B. The LLM is a custom model that Nutanix has not validateD.
C. The NVIDIA GPU Operator is not installed on the Kubernetes cluster.
D. The system is awaiting the necessary cluster resources.

Answer: D

NEW QUESTION # 37
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