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GitHub Administration
- Exam Number/Code : GH-100
- Exam Name : GitHub Administration
- Questions and Answers : 213 Q&As
- Update Time: 2019-01-10
- Price:
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NEW QUESTION: 1
ABC Corp. has implemented Oracle Manufacturing applications from Oracle E-Business Suite Release 12. How is the data in the manufacturing applications partitioned?
A. BY Application
B. By Legal Entity
C. By Operating Unit
D. By Inventory Organization
E. By Business Group
Answer: D
NEW QUESTION: 2
Which two aspects are considered when designing a dual hub, dual DMVPN cloud topology? (Choose two.)
A. requires all sites to have dual Internet connections
B. will only work with single-tier headend architecture
C. spoke-to-spoke traffic will transit the hub unless spokes exchange dynamic routing directly
D. hub sites must connect to both DMVPN clouds
E. recommended for high availability
Answer: B,E
NEW QUESTION: 3
How would a developer invoke the adapter from the application?
A. WL.Client.invokeAdapter (invocationData, options)
B. WL.Client.invokeProcedure (invocationData, options)
C. WL.Environment.invokeAdapter (invocationData, options)
D. WL.Environment.invokeProcedure (invocationData, options)
Answer: B
NEW QUESTION: 4
Why do you need to split a machine learning dataset into training data and test data?
A. To make sure your model is generalized for more than just the training data
B. So you can use one dataset for a wide model and one for a deep model
C. To allow you to create unit tests in your code
D. So you can try two different sets of features
Answer: A
Explanation:
Explanation
The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.
Reference: https://machinelearningmastery.com/a-simple-intuition-for-overfitting/