- Exam Code: HPE2-T38
- Exam Name: HPE AI and Machine Learning
- Updated: Jul 23, 2026
- Q & A: 108 Questions and Answers
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| Section | Objectives |
|---|---|
| Topic 1: Artificial Intelligence Fundamentals | - Core AI concepts and terminology - AI use cases in enterprise environments - Types of machine learning (supervised, unsupervised, reinforcement learning) |
| Topic 2: Data Engineering for AI | - Data preprocessing and feature engineering - Data pipelines and storage considerations |
| Topic 3: Machine Learning Lifecycle | - Model training, validation, and evaluation - Data collection and preparation - Model deployment and monitoring |
| Topic 4: HPE AI and Machine Learning Solutions | - Hybrid cloud AI infrastructure concepts - HPE Ezmeral for ML operations - HPE GreenLake for AI workloads |
| Topic 5: MLOps and Operationalization | - Automation and CI/CD for ML - Monitoring and governance of ML systems - Model lifecycle management |
1. What is essential for troubleshooting in the HPE ML PDK?
Response:
A) Contacting HPE for every minor issue
B) Guesswork and frequent trial-and-error
C) Outsourcing problem resolution
D) Access to comprehensive troubleshooting guides and support
2. What is the first step in using the HPE ML PDK?
Response:
A) Directly starting with model training
B) Installing the necessary software and libraries
C) Reading the user manual
D) Configuring the network settings
3. How does the HPE ML PDK support model testing and validation?
Response:
A) It does not include testing and validation tools
B) Only through external software integrations
C) By recommending manual testing methods
D) By providing robust tools for model testing and validation
4. What does the HPE Ezmeral Unified Analytics Software primarily offer?
Response:
A) Basic data processing tools
B) Managed, secure enterprise-grade open-source frameworks
C) Hardware optimization for ML models
D) Data encryption services
5. Which algorithm is commonly used for classification problems?
Response:
A) Support Vector Machines
B) Apriori Algorithm
C) Principal Component Analysis
D) Linear Regression
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: A |
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