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NEW QUESTION: 1
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. To make sure your model is generalized for more than just the training data
C. So you can use one dataset for a wide model and one for a deep model
D. To allow you to create unit tests in your code
Answer: B
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/

NEW QUESTION: 2
When processing an auto accident claim, the system requires three approvals: Approval from the adjuster who inspected the car, approval from the medical administrator who provided medical care to the insured, and approval from the insurance agent who verified the claim.
How do you implement this requirement so each approver can work independently?
A. Create three assignments and route to the appropriate approver
B. Create business logic to route to the different approvers
C. Create an approval step with cascading approval
D. Route to a workbasket where all three roles have access
Answer: C

NEW QUESTION: 3
A new security administrator ran a vulnerability scanner for the first time and caused a system outage. Which of the following types of scans MOST likely caused the outage?
A. Non-intrusive non-credentialed scan
B. Non-intrusive credentialed scan
C. Intrusive non-credentialed scan
D. Intrusive credentialed scan
Answer: C