HP HPE2-T38 Exam Overview:
| Certification Vendor: | HPE |
| Exam Name: | HPE AI and Machine Learning |
| Exam Number: | HPE2-T38 |
| Exam Duration: | 90 minutes |
| Related Certifications: | HPE Product Certified - AI and Machine Learning |
| Exam Format: | Multiple Choice |
| Real Exam Qty: | 50 |
| Passing Score: | 70% |
| Available Languages: | English, Japanese, Korean |
| Sample Questions: | HP HPE2-T38 Sample Questions |
| Exam Way: | Web-based online exam |
| Pre Condition: | Recommended to have foundational knowledge of AI, machine learning, and HPE machine learning solutions. |
| Official Syllabus URL: | https://education.hpe.com/ww/en/training/docs/cds/h61x2s.pdf |
HP HPE2-T38 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Describe Requirements and Prerequisites for HPE ML Solutions | 13% | - Architecture and deployment
- 1. Deployment options
- 2. HPE machine learning architecture
- 3. Infrastructure requirements
|
| Compare HPE ML Enterprise Offerings to Open-Source Versions | 7% | - Enterprise vs open-source comparison
- 1. Enterprise features and advantages
- 2. Scalability and management capabilities
|
| Understand Machine Learning Ecosystem Fundamentals | 13% | - Machine learning technology fundamentals
- 1. Challenges in deep learning model training
- 2. Components of machine learning ecosystems
- 3. Machine learning and deep learning concepts
|
| Examine HPE Machine Learning Offerings | 15% | - HPE AI and ML portfolio
- 1. Aligning HPE ML solutions to customer goals
- 2. Capabilities of HPE AI-at-scale portfolio
- 3. HPE machine learning deployment solutions
|
| Engage with Customers | 10% | - Customer engagement and proof of concept
- 1. Qualifying customers for HPE AI offerings
- 2. Running proof-of-concept demonstrations
- 3. Identifying engagement personas
|
| Articulate the Business Value of HPE ML Solutions | 24% | - Business value and positioning
- 1. HPE AI market positioning
- 2. Benefits of Machine Learning Development Management System
- 3. Benefits of Machine Learning Development Environment
|
| Demonstrate and Explain How to Use HPE Machine Learning PDK | 18% | - Using HPE ML PDK
- 1. Training machine learning models
- 2. Model deployment
- 3. Data versioning and lineage
- 4. Automation and integration workflows
|
HPE AI and Machine Learning Sample Questions:
1. Which of these is a type of supervised learning?
Response:
A) Dimensionality Reduction
B) Regression
C) Association
D) Clustering
2. What distinguishes HPE Machine Learning solutions in terms of maintenance and updates compared to open-source versions?
Response:
A) No difference in maintenance and updates
B) Maintenance is more challenging with HPE solutions
C) Open-source versions offer more frequent updates
D) HPE provides regular, professional updates
3. How do HPE ML solutions contribute to a business''s scalability?
Response:
A) By focusing solely on short-term goals
B) By offering scalable solutions that grow with the business
C) By limiting the business to current operations
D) By increasing operational costs disproportionately
4. How can you access documentation for the HPE ML PDK?
Response:
A) All of the above
B) In the product packaging
C) Through the HPE website
D) By contacting HPE customer support
5. What is a key feature of HPE Ezmeral Unified Analytics Software?
Response:
A) Consistent SaaS experience
B) Graphic design capabilities
C) Email marketing tools
D) Social media analytics
Solutions:
Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: A |
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