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 sections, you will NOT be able to return to it. As a result, these questions will
not appear in the review screen.
You are working on an Azure Machine Learning experiment.
You have the dataset configured as shown in the following table.
You need to ensure that you can compare the performance of the models and add annotations to the results.
Solution: You connect the Score Model modules from each trained model as inputs for the Evaluate Model
module, and use the Execute R Script module.
Does this meet the goal?
A.
Yes
B.
No
Explanation:
Explanation/Reference:https://msdn.microsoft.com/en-us/library/azure/dn905915.aspx
Answer is Yes
1
0
Yes.
1
0
BTW, part of that new 45Q 70-774 dumps are available here:
https://drive.google.com/open?id=1kasCFEWbbVbNGXhtQ5AfwMOgYaKryNdH
Best Regards!
1
0
The Correct answer is NO. (Trick wording)
No need of Execute R module:
By adding R code to this module, you can perform a variety of customized tasks that are not available in Studio. For example:
– Create custom data transformations
– Use your own metrics for evaluating predictions
– Build models using algorithms that are not implemented as standalone modules in Studio
Score Model:
After you have generated a set of scores using Score Model:
To generate a set of metrics used for evaluating the model’s accuracy (performance). you can connect the scored dataset to Evaluate Model
Evaluate Mode:
To evaluate a model, you must connect a dataset that contains a set of input columns and scores. If no other data is available, you can use your original dataset.
– Connect the Scored datset output of the Score Model to the input of Evaluate Model.
– Click Evaluate Model module, and select Run selected to generate the evaluation scores.
1
0