HP HPE2-T38 Questions & Answers - in .pdf
- Vendor: HP
- Exam Code: HPE2-T38
- Exam Name: HPE AI and Machine Learning
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HP HPE2-T38 Exam Overview:
| Certification Vendor: | Hewlett Packard Enterprise (HPE) |
| Exam Name: | HPE AI and Machine Learning |
| Exam Number: | HPE2-T38 |
| Exam Format: | Multiple choice, Drag and drop, Fill in the blank |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 90 minutes |
| Passing Score: | 70% |
| Real Exam Qty: | 50 |
| Available Languages: | English, Japanese, Korean |
| Exam Price: | $75 - $140 USD |
| Recommended Training: | HPE AI and Machine Learning Training Course |
| Exam Registration: | Pearson VUE Registration |
| Sample Questions: | HP HPE2-T38 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No mandatory prerequisites; recommended knowledge of AI/ML concepts and HPE solutions |
| Official Syllabus URL: | https://www.hpe.com/us/en/certification/exams/hpe2-t38.html |
HP HPE2-T38 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| HPE Machine Learning Offerings | 15% | - Aligning HPE solutions with customer requirements - Different deployment models for HPE solutions - Capabilities of HPE AI at-scale portfolio software |
| Business Value of HPE Machine Learning Solutions | 24% | - Differentiators compared to alternative solutions - Accelerated innovation and time-to-value - Cost optimization and efficiency benefits |
| HPE vs Open Source Machine Learning Solutions | 7% | - Enterprise readiness and support - Scalability and performance differences - Feature comparison |
| Requirements and Prerequisites for HPE Machine Learning Solutions | 13% | - Infrastructure and environment prerequisites - Hardware and software requirements |
| Machine Learning Ecosystem Fundamentals | 13% | - Basics of machine learning technology - Challenges in training deep learning models - Components of a machine learning ecosystem |
| Using HPE Machine Learning Professional Development Kit (PDK) | 18% | - Model training and optimization - Model deployment and monitoring - Automation and integration workflows - PDK fundamentals and architecture - Data versioning and lineage management |
HPE AI and Machine Learning Sample Questions:
1. ''Cross-validation'' in ML is used for:
Response:
A) Data encryption
B) Data recovery
C) Algorithm selection
D) Model tuning
2. What does ''ensemble learning'' refer to in ML?
Response:
A) Learning from a single model
B) A method for data preprocessing
C) Training models sequentially
D) Combining predictions from multiple models
3. What is an important consideration for data privacy when using the HPE ML PDK?
Response:
A) Using only public data
B) Data privacy is automatically managed
C) Ignoring data privacy
D) Adhering to data privacy standards and regulations
4. What is ''bias'' in the context of ML?
Response:
A) The cost of deploying a model
B) The storage capacity required for a model
C) A model,s preference for certain data
D) The programming language used to build a model
5. Which of the following is an example of unsupervised learning?
Response:
A) Decision Trees
B) Logistic Regression
C) Linear Regression
D) K-Means Clustering
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: D |
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