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Last Updated: Sep 22, 2026

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HPE2-N69 is a certification exam that focuses on using the HPE Cray AI Development Environment. HPE Cray is a leading provider of high-performance computing solutions, and the certification exam is designed to validate the skills and knowledge of individuals who work with HPE Cray AI Development Environment. HPE2-N69 exam is intended for developers, data scientists, and other IT professionals who want to demonstrate their expertise in using HPE Cray AI Development Environment to develop, deploy, and manage machine learning and deep learning applications.

HPE2-N69 exam comprises 40 multiple-choice questions, which the candidate must complete within 60 minutes. HPE2-N69 exam is available in English and Japanese languages and can be taken at proctored exam centers worldwide. HP recommends that individuals have at least three years of experience in developing and deploying AI workloads before attempting the HPE2-N69 certification exam.

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HPE Cray AI Development Environment is a powerful tool that provides a comprehensive set of tools and capabilities for building and deploying AI solutions. It is designed to be highly scalable and flexible, allowing developers to work with a wide range of data types and models. The HPE Cray AI Development Environment also provides a range of features that allow developers to streamline their workflows and improve their productivity.

Reference: https://certification-learning.hpe.com/tr/datacard/exam/HPE2-N69

HP HPE2-N69 Exam Syllabus Topics:

SectionWeightObjectives
Solution Qualification & Sizing15%- Position against market alternatives
- Perform basic sizing and configuration
- Qualify customers for HPE ML solutions
Environment Usage & Workload Management33%- Run and manage different types of experiments
- Understand resource allocation and scheduling
- Navigate web UI and CLI tools
Architecture and Deployment15%- Differentiate from open-source Determined AI
- Explain deployment options and system components
- Describe software architecture of HPE Cray AI/ML Environment
ML and DL Fundamentals & Customer Challenges24%- Explain core machine learning and deep learning concepts
- Identify common challenges in training deep learning models
- Articulate business value and use cases for HPE solutions
Integration and Operations13%- Manage data and model lifecycle
- Monitor cluster health and performance

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