Quiz Accurate NCP-AI - Test Nutanix Certified Professional - Artificial Intelligence v6.10 Practice

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Nutanix NCP-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	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 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 (Q35-Q40):

NEW QUESTION # 35
An NAI administrator has successfully imported a model from Hugging Face and created an endpoint for the model. The endpoint is in the Active state. From within the Endpoint section in NAI, the endpoint has been tested with a Sample Request, the response is accurate, and the Status shows SucceedeD.
The administrator has provided the endpoint URL and generated and provided API keys to the developers. However, the developers are having issues connecting to the endpoint. They keep getting 400 Bad Request errors when attempting to prompt the model.
What should the administrator do next to ensure the developers are able to successfully prompt the model?

A. Add additional resources to 'scale up' the number of instances of the endpoint.
B. Check the Status of the endpoint again to make sure it was not accidentally HibernateD.
C. Provide the developers the 'Sample Request' code from the endpoint by clicking 'View Sample Request'.
D. Re-import the model from Hugging Face, create a new endpoint and provide new URL and API keys to the developers.

Answer: C

NEW QUESTION # 36
An administrator has installed and deployed NAI, but the UI is not launching.
Which command should the administrator use to ensure that all pods are successfully deployed?

A. kubectl get pods -n knative-serving
B. kubectl get pods -n kube-system
C. kubectl get pods -n nai-system
D. kubectl get pods -n ntnx-base

Answer: C

NEW QUESTION # 37
Which deployment type of Nutanix Enterprise AI is supported in Amazon EKS?

A. Standalone
B. Cloud Cluster
C. Bare Metal
D. GPT-in-a-Box 2.0

Answer: B

NEW QUESTION # 38
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What does the View Sample Request link within the Endpoint List provide for administrators?

A. It includes the instructions on how to delete the endpoint.
B. It contains the URL and API key to connect to the endpoint.
C. It connects the endpoint to the Large Language Model.
D. It only stores the URL required to access the endpoint.

Answer: B

NEW QUESTION # 39
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An Accounting Department is thrilled with the RAG Application that the Application & Data Science Teams recently rolled out. However, they provided some feedback that sometimes (approximately 20% of the time), the documents retrieved are not relevant to their prompts or are too generiC.
During development, there was extensive testing between models to make sure the best possible model was selecteD. The Accounting Department emphasizes that when the responses use the right documents, the results are very good and they are pleased with the completeness, accuracy, and coherence of those responses.
What would be a way to address the irrelevant RAG results without having to rebuild the entire workflow?

A. Fine-tune the Large Language Model on a broader dataset to enable it to generate more relevant responses.
B. Implement a rerank model as a post retrieval step to re-order initially retrieved documents based on query-document relevance.
C. Replace the embedding model with a larger, more general-purpose language model to improve document retrieval.
D. Significantly expand the document knowledge base by ingesting a much larger volume of financial reports.

Answer: B

NEW QUESTION # 40
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