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AWS Certified Machine Learning - Specialty

  • Exam Number/Code : MLS-C01
  • Exam Name : AWS Certified Machine Learning - Specialty
  • Questions and Answers : 213 Q&As
  • Update Time: 2019-01-10
  • Price: $ 99.00 $ 39.00

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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 Operating Unit
C. By Inventory Organization
D. By Business Group
E. By Legal Entity
Answer: C

NEW QUESTION: 2
Which two aspects are considered when designing a dual hub, dual DMVPN cloud topology? (Choose two.)
A. hub sites must connect to both DMVPN clouds
B. recommended for high availability
C. spoke-to-spoke traffic will transit the hub unless spokes exchange dynamic routing directly
D. will only work with single-tier headend architecture
E. requires all sites to have dual Internet connections
Answer: B,D

NEW QUESTION: 3
How would a developer invoke the adapter from the application?
A. WL.Client.invokeAdapter (invocationData, options)
B. WL.Environment.invokeProcedure (invocationData, options)
C. WL.Client.invokeProcedure (invocationData, options)
D. WL.Environment.invokeAdapter (invocationData, options)
Answer: C

NEW QUESTION: 4
Why do you need to split a machine learning dataset into training data and test data?
A. So you can try two different sets of features
B. So you can use one dataset for a wide model and one for a deep model
C. To make sure your model is generalized for more than just the training data
D. To allow you to create unit tests in your code
Answer: C
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/