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NEW QUESTION: 1
Given:
11.public static void main(String[] args) {
12.Integer i = new Integer(1) + new Integer(2);
13.switch(i) {
14.case 3: System.out.println("three"); break;
15.default: System.out.println("other"); break;
16.}
17.}
What is the result?
A. Compilation fails because of an error on line 15.
B. three
C. Compilation fails because of an error on line 13.
D. Compilation fails because of an error on line 12.
E. other
F. An exception is thrown at runtime.
Answer: B

NEW QUESTION: 2



A. Option A
B. Option D
C. Option B
D. Option C
Answer: D

NEW QUESTION: 3
Web hosting is a service that enables customers to deploy a website quickly. This is an example of:
A. PaaS.
B. On-demand software.
C. SaaS
D. IaaS.
E. None of the above.
Answer: A

NEW QUESTION: 4
You need to correct the model fit issue.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:
Explanation:

Explanation:
Step 1: Augment the data
Scenario: Columns in each dataset contain missing and null values. The datasets also contain many outliers.
Step 2: Add the Bayesian Linear Regression module.
Scenario: You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.
Step 3: Configure the regularization weight.
Regularization typically is used to avoid overfitting. For example, in L2 regularization weight, type the value to use as the weight for L2 regularization. We recommend that you use a non-zero value to avoid overfitting.
Scenario:
Model fit: The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.
Incorrect Answers:
Multiclass Decision Jungle module:
Decision jungles are a recent extension to decision forests. A decision jungle consists of an ensemble of decision directed acyclic graphs (DAGs).
L-BFGS:
L-BFGS stands for "limited memory Broyden-Fletcher-Goldfarb-Shanno". It can be found in the wwo-Class Logistic Regression module, which is used to create a logistic regression model that can be used to predict two (and only two) outcomes.
References:
<https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regr ession>