Aspiring professionals seeking to obtain the prestigious HPE Product Certified - AI and Machine Learning certification must complete the HPE2-T38 HPE AI and Machine Learning exam. To support exam takers in their preparation journey, PassQuestion offers an extensive collection of the most up-to-date HPE AI and Machine Learning HPE2-T38 Real Questions which are designed to boost candidates' confidence, enhance their understanding of key concepts, and ultimately facilitate a smooth and successful exam experience. By leveraging PassQuestion HPE AI and Machine Learning HPE2-T38 Real Questions, you can approach the HPE2-T38 exam with a heightened sense of readiness, minimizing potential challenges and maximizing their chances of achieving certification on your first attempt.

This certification verifies that you can design and support solutions using HPE AI and Machine Learning Development Environment to easily implement and train machine learning models by removing complexities, optimizing cost, and accelerating innovation. The ideal candidates for this exam are involved in technical presales, including those who can design and demonstrate machine learning solutions, and execute POCs, across the Machine Learning stack. The candidates can align relevant HPE Machine Learning solutions to customer goals and explain the unique benefits of a proposed solution in a way that the technical buyer can understand.

Exam Information

Exam ID: HPE2-T38
Exam type: Web-based
Exam duration: 1 hour 30 minutes
Exam length: 50 questions
Passing score: 70%
Delivery languages: English, Japanese, Korean

Exam Objectives

Understand machine learning (ML) ecosystem fundamentals:  13%

• 1.1 Recognize the fundamentals of the technology.
• 1.2 Identify the challenges customers face in training DL models.
• 1.3 Classify Potential Components of an ML ecosystem.

Examine the HPE ML Offerings:         15%   

• 2.1 Recite key capabilities of HPEs AI at-scale portfolio software
• 2.2 Align relevant HPE ML solutions to customer goals
• 2.3 Recognize different HPE deployment solutions 

Describe requirements and prerequisites for HPE machine learning solutions:     13%

• 3.1 Compare HPE machine learning (ML) architecture and deployment options.
• 3.2 Recognize some common factors regarding required infrastructures.

Articulate the business value of HPE ML solutions:        24%

• 4.1 Articulate the benefits of MLDMS 
• 4.2 Articulate the benefits of MLDE 
• 4.3 Describe how HPE AI offerings fit in the market.

Demonstrate and explain how to use HPE machine learning (ML) [PDK]:      18%

• 5.1  Explain the fundamentals of PDK 
• 5.2  Demonstrate an ability to engage with data versioning and lineage 
• 5.3  Explain how to train a new model 
• 5.4  Explain how to deploy the model
• 5.5  Demonstrate ability to automate and integrate these steps for deployment

Compare HPE Machine Learning enterprise offerings to open-source versions:    7%

• 6.1  Describe Current Enterprise features

Engage with customers:      10%

• 7.1    Qualify customers for HPE AI offerings
• 7.2    Identify the appropriate personas for engagement 
• 7.3    Demonstrate a proof of concept (PoC)

View Online HPE AI and Machine Learning HPE2-T38 Free Questions

1. Which aspect of HPE's machine learning solutions can help businesses in developing a better understanding of customer needs and preferences?
A. Integration with CRM systems
B. Algorithm transparency
C. Automated model training
D. Real-time data processing
Answer: A

2. How can HPE ML solutions contribute to revenue growth for businesses?
A. Predicting customer churn
B. Recommending cross-sell opportunities
C. Identifying upsell opportunities
D. All of the above
Answer: D

3. Which of the following is NOT a type of machine learning algorithm?
A. Supervised learning
B. Reinforcement learning
C. Pre-defined learning
D. Unsupervised learning
Answer: C

4. What is the role of a hidden layer in an artificial neural network (ANN)?
A. It receives and weighs inputs from the preceding layer and produces outputs for the next layer.
B. It assigns the label to a record, or in other words, produces the final result for the ANN.
C. It ingresses parameters from a record and passively reformats those parameters without any changes over the training process.
D. It does not play a role during the forward pass of data through the ANN, but it helps to optimize during the backward pass.
Answer: A

5. For large-scale machine learning solutions, what type of data handling capability is essential?
A. Ability to handle small, static datasets only
B. Capability to process and analyze large, dynamic datasets
C. Only cloud-based data handling is required
D. The ability to process data is not important
Answer: B

6. What is a significant benefit of HPE ML solutions in terms of innovation?
A. They discourage new ideas
B. They streamline the innovation process
C. They focus only on existing technologies
D. They reduce the pace of technological advancement
Answer: B

7. What is the primary goal of customer engagement?
A. To increase product prices
B. To maximize short-term sales
C. To build long-term customer relationships
D. To reduce marketing costs
Answer: C

8. Which of these is a crucial software prerequisite for running machine learning models?
A. Web browsers
B. Office suites
C. Machine learning libraries and frameworks
D. Email clients
Answer: C 

9. HPE Machine Learning Data Management Software is designed to:
A. Streamline HR processes
B. Process petabyte-scale workloads
C. Focus on cybersecurity
D. Handle supply chain management
Answer: B

10. What are HPE ProLiant Servers best suited for?
A. High-frequency trading
B. Educational platforms
C. AI tasks like visual AI and language processing
D. Website development
Answer: C