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NEW QUESTION: 1
You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How can you make your production model more accurate?
A. Split the training and test data based on time rather than a random split to avoid leakage
B. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.
C. Add more data to your test set to ensure that you have a fair distribution and sample for testing
D. Normalize the data for the training, and test datasets as two separate steps.
Answer: B
NEW QUESTION: 2
In order to the leverage the power of data correlation with Nessus, a cybersecurity analyst must first be able to create a table for the scan results.
Given the following snippet of code:
Which of the following output items would be correct?
A. 
B. 
C. 
D. 
Answer: C
NEW QUESTION: 3
You are developing an ASP.NET MVC application that enables you to edit and save a
student object.
The application must not retrieve student objects on an HTTP POST request.
You need to implement the controller.
Which code segment should you use? (Each correct answer presents a complete solution.
Choose all that apply.)
A. Option A
B. Option C
C. Option D
D. Option B
Answer: A,B