The Nutanix NCP-AI exam preparation guide is designed to provide candidates with necessary information about the Artificial Intelligence exam. It includes exam summary, sample questions, practice test, objectives and ways to interpret the exam objectives to enable candidates to assess the types of questions-answers that may be asked during the Nutanix Certified Professional - Artificial Intelligence (NCP-AI) exam.
It is recommended for all the candidates to refer the NCP-AI objectives and sample questions provided in this preparation guide. The Nutanix Artificial Intelligence certification is mainly targeted to the candidates who want to build their career in Professional Level domain and demonstrate their expertise. We suggest you to use practice exam listed in this cert guide to get used to with exam environment and identify the knowledge areas where you need more work prior to taking the actual Nutanix Certified Professional - Artificial Intelligence exam.
Nutanix NCP-AI Exam Summary:
Nutanix Artificial Intelligence Syllabus:
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Section |
Objectives |
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Deploy a Nutanix Enterprise AI Environment
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- Validate installation prerequisites
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Identify the installation prerequisites
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Identify the installation limitations
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Cite the installation procedure
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Describe the core fundamental components of NAI architecture
- Install Nutanix Enterprise AI components
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Compare and contrast the installation process for NKP (including app catalog) and non NKP environments
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Recognize version compatibility between pre-requisite and NAI components
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Perform a dark site installation
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Configure storage classes
- Configure DNS, setup the URL, and manage required certificates
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Identify or implement an FQDN for the NAI installation
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Ensure that the FQDN has a secure certificate
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Validate successful login to UI
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Configure a Nutanix Enterprise AI Environment |
- Onboard users to Nutanix Enterprise AI
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Differentiate between the user and administrator roles
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Identify the user management operations that can be performed as an administrator
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Given specific scenarios, leverage roles to limit privileges for target users
- Import Large Language Models (LLMs)
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Recognize the methods and repos available for importing
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Obtain repo keys for HuggingFace and/or NVIDIA NGC
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Recognize where to add repo keys in the UI for usage
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Explain the manual import process and the requirements
- Create endpoints
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Determine a downloaded model to expose via an endpoint
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Determine the number and type of GPUs required for a selected model
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Determine the number of instances required to achieve a certain throughput
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Determine vCPU/memory and inference engine for optimization scenarios
- Create and apply keys for each API endpoint
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Identify the locations to generate and manage API keys
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Identify where to view API keys in an endpoint
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Deactivate an API key
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Add an API key to an existing endpoint
- Deliver endpoints to the consumer
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Identify the endpoint URI and model-specific parameters and the API key to be shared with consumers
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Identify tool calling vs non tool calling API curl commands
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Perform Day 2 Operations
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- Prepare requirements for connecting the app
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Determine where to get the sample request in the NAI application
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Explain the elements in the sample request and the elements required for the OpenAI compliant application configuration
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Recognize the different endpoint types and choose the correct one for a given application
- Interpret performance details and optimize accordingly
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Determine the observability metrics for performance evaluation
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Determine possible changes in resource allocation to remedy latency and throughput issues
- Monitor access activity for outlier detection
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Determine where and how to view the top 5 API Keys being used
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Locate the endpoint dashboard and view assigned API keys
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Recognize when to deactivate API keys
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Review and interpret audit events
- Select the appropriate LLM to optimize output quality
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Determine the prompt input and the LLM output per endpoint to evaluate accuracy through human feedback
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Determine techniques and models that can be used to improve the output quality
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Apply guardrails to improve safety
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Apply rerank models to achieve desired results
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Troubleshoot a Nutanix Enterprise AI Environment |
- Troubleshoot and resolve performance and resource utilization issues
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Determine where to view infrastructure performance
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Recognize how to filter by GPU nodes and review resulting GPU utilization graph to determine which GPUs are being heavily used
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Determine if an endpoint is using GPU
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Recognize which type of GPU an endpoint is using.
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Determine if endpoint is using CPU-based acceleration or not
- Remediate health check failures on the cluster
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Debug a cluster health fail visible on NAI UI
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Recognize the different components that can cause health check failures
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Analyze Kubernetes NAI system resources to address health check failures
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Determine which layer of the stack is causing the health check failure
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Based on a health check failure diagnosis, determine an appropriate course of action
- Troubleshoot model import and endpoint creation
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Identify the failure scenarios where model download fails due to misconfigurations and/or connectivity (e.g., prevalidated, custom, and/or restricted networks)
- Troubleshoot CSI driver connectivity
- Ensure model EULA was accepted on HuggingFace (Llama models)
- Ensure HuggingFace or NVIDIA token is valid
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Determine available allocatable compute resources (e.g., CPU, memory, GPUs, taints) that could prevent endpoints from being scheduled
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Recognize if all prerequisites were successfully installed (e.g. Kserve)
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Diagnose the cause of container images failing to be downloaded or be stored on Kubernetes nodes
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Connect Applications to a Nutanix Enterprise AI Environment
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- Configure and validate an application with the endpoint
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Differentiate between model and endpoint types to be consumed by the application
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Recognize the purpose and use case for integrating the various types of models
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Issue a simple query to the OpenAI-compatible NAI API endpoints using Python or Curl
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Investigate and address application integration issues
- Check endpoint metrics corresponding to application usage
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Identify the latency and number of API requests per endpoint associated with application
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Describe how to correlate the application with NAI endpoint metrics
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