Practice Free HPE2-T38 Exam Online Questions
How can HPE ML solutions enhance cybersecurity measures for organizations?
- A . Reducing the need for security protocols
- B . Increasing vulnerability to cyber attacks
- C . Detecting and mitigating threats in real-time
- D . Improving physical security measures
C
Explanation:
Detecting and mitigating threats in real-time is how HPE ML solutions can enhance cybersecurity measures for organizations.
How does the HPE Machine Learning [PDK] handle model versioning and tracking?
- A . It deletes previous versions to save storage space.
- B . It does not support model versioning.
- C . It requires manual tracking by the developer.
- D . It automatically saves all versions of a model and tracks performance metrics.
D
Explanation:
The HPE Machine Learning [PDK] automatically saves all versions of a model and tracks performance metrics for easy comparison and management.
Why is it important to have a scalable infrastructure when implementing HPE machine learning solutions?
- A . To avoid using cloud services
- B . To handle increasing data volumes and computational demands
- C . To limit the number of users accessing the system
- D . To prioritize aesthetic design over functionality
B
Explanation:
Having a scalable infrastructure when implementing HPE machine learning solutions is important to handle increasing data volumes and computational demands.
What is the purpose of data preprocessing in machine learning?
- A . To evaluate model performance
- B . To deploy machine learning solutions
- C . To build machine learning models
- D . To clean and transform raw data
D
Explanation:
The purpose of data preprocessing in machine learning is to clean and transform raw data before feeding it into the model.
How does the HPE Machine Learning PDK help with the deployment of machine learning models?
- A . By automating the model training process
- B . By organizing data sets
- C . By generating code for deployment
- D . By providing pre-built algorithms
C
Explanation:
The HPE Machine Learning PDK helps with deployment by generating code for deploying machine learning models.
How can HPE ML solutions contribute to better customer insights and engagement?
- A . Analyzing customer preferences
- B . All of the above
- C . Improving customer segmentation
- D . Personalizing communication
B
Explanation:
HPE ML solutions can contribute to better customer insights and engagement by analyzing customer preferences, personalizing communication, and improving customer segmentation.
Why is domain expertise considered a prerequisite for effectively deploying HPE machine learning solutions?
- A . It speeds up training time
- B . It helps in understanding the nuances of the business problem
- C . It reduces the need for model evaluation
- D . It eliminates the need for data preprocessing
B
Explanation:
Domain expertise is considered a prerequisite for effectively deploying HPE machine learning solutions as it helps in understanding the nuances of the business problem.
In terms of deployment and implementation, which option is typically easier to set up: HPE Machine Learning enterprise offerings or open-source versions?
- A . Open-source versions
- B . HPE Machine Learning enterprise offerings
B
Explanation:
HPE Machine Learning enterprise offerings are typically easier to set up and deploy compared to open-source versions.
How can HPE’s machine learning solutions help businesses engage with their customers more effectively?
- A . Personalized recommendations
- B . Faster time to market
- C . Enhanced customer service
- D . Predictive analytics
D
Explanation:
HPE’s machine learning solutions enable businesses to engage with customers more effectively by utilizing predictive analytics for insights into customer behavior.
What type of infrastructure is needed to support HPE machine learning solutions?
- A . No specific infrastructure required
- B . Both cloud-based and on-premises infrastructure
- C . Cloud-based infrastructure only
- D . On-premises infrastructure only
B
Explanation:
Both cloud-based and on-premises infrastructure are needed to support HPE machine learning solutions.