Practice Free HPE2-T38 Exam Online Questions
How does the HPE Machine Learning Platform Development Kit (PDK) assist in model deployment?
- A . By providing tools for data cleaning
- B . By ensuring network security
- C . By simplifying the process of deploying machine learning models
- D . By offering visualization capabilities
C
Explanation:
The HPE Machine Learning Platform Development Kit (PDK) assists in model deployment by simplifying the process of deploying machine learning models.
What is one of the key business values of HPE ML solutions in terms of efficiency and productivity?
- A . Improving decision-making
- B . Lowering costs
- C . Enhancing customer experience
- D . Increasing operational efficiency
D
Explanation:
Increasing operational efficiency is a key business value of HPE ML solutions as it can automate processes and streamline operations.
In what way can HPE ML solutions contribute to enhancing customer experience?
- A . By improving product recommendations
- B . By ignoring customer feedback
- C . By reducing personalization
- D . By increasing response time
A
Explanation:
HPE ML solutions can contribute to enhancing customer experience by improving product recommendations.
What is one of the main components of HPE’s machine learning offerings?
- A . HPE ML Ops
- B . HPE ML data visualization tool
- C . HPE ML algorithm repository
- D . HPE ML model builder
A
Explanation:
HPE ML Ops is one of the main components of HPE’s machine learning offerings.
How can HPE machine learning solutions contribute to creating a more data-driven culture within an organization?
- A . By making data-driven decisions obsolete
- B . By ignoring customer feedback
- C . By avoiding data analysis and insights
- D . By democratizing access to data and insights
D
Explanation:
HPE machine learning solutions can contribute to creating a more data-driven culture within an organization by democratizing access to data, enabling employees at all levels to make data-backed decisions.
Can the HPE machine learning [PDK] be used for both supervised and unsupervised learning tasks?
- A . Yes, but separate versions are required
- B . No, it is designed for unsupervised learning only
- C . No, it is designed for supervised learning only
- D . Yes, it supports both types of learning
D
Explanation:
The HPE machine learning [PDK] can be used for both supervised and unsupervised learning tasks, as it supports both types of learning.
What is one of the key benefits of implementing HPE ML solutions in terms of scalability?
- A . All of the above
- B . Improving data processing speed
- C . Scaling resources based on demand
- D . Enhancing system performance
C
Explanation:
One of the key benefits of implementing HPE ML solutions is scalability, allowing businesses to scale resources based on demand.
Why is it important for customers to review the system requirements before implementing HPE machine learning solutions?
- A . To increase the cost of implementation
- B . To ensure compatibility with existing hardware and software
- C . To delay the deployment process
- D . To limit the functionality of the ML solutions
B
Explanation:
Reviewing system requirements before implementing HPE machine learning solutions is crucial to ensure compatibility with existing hardware and software, facilitating a smooth and successful deployment.
What is the main goal of machine learning?
- A . To create advanced artificial intelligence systems
- B . To develop algorithms for processing and analyzing big data
- C . To enable computers to learn from data and improve over time
- D . To program computers to perform specific tasks
C
Explanation:
The main goal of machine learning is to enable computers to learn from data and improve over time.
Which HPE ML offering provides a platform for managing, controlling, and orchestrating AI and ML workflows in hybrid cloud environments?
- A . HPE Ezmeral Data Fabric Edge
- B . HPE Ezmeral ML Ops
- C . HPE Ezmeral Data Fabric
- D . HPE Ezmeral Container Platform
B
Explanation:
HPE Ezmeral ML Ops provides a platform for managing, controlling, and orchestrating AI and ML workflows in hybrid cloud environments.