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NEW QUESTION: 1
Given:
11. public void go(int x) {
12. assert (x > 0);
13. switch(x) {
14. case 2: ;
15. default: assert false;
16. }
17. }
18. private void go2(int x) { assert (x < 0); }
Which statement is true?
A. All of the assert statements are used appropriately.
B. Only the assert statement on line 15 is used appropriately.
C. Only the assert statements on lines 12 and 18 are used appropriately.
D. Only the assert statements on lines 12 and 15 are used appropriately.
E. Only the assert statement on line 18 is used appropriately.
F. Only the assert statements on lines 15 and 18 are used appropriately.
G. Only the assert statement on line 12 is used appropriately.
Answer: F
NEW QUESTION: 2
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.
You must run the script as an Azure ML experiment on a compute cluster named aml-compute.
You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.
Solution: Run the following code:
Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Explanation
The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target. It is implemented through the SKLearn class, which can be used to support single-node CPU training.
Example:
from azureml.train.sklearn import SKLearn
}
estimator = SKLearn(source_directory=project_folder,
compute_target=compute_target,
entry_script='train_iris.py'
)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn
NEW QUESTION: 3
A. Option B
B. Option C
C. Option A
D. Option D
Answer: C
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