Microsoft 70-774 Exam Practice Questions (P. 2)
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Question #6
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 then save the results as a dataset.
Does this meet the goal?
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 then save the results as a dataset.
Does this meet the goal?
- AYes
- BNo
Correct Answer:
A
References:
https://msdn.microsoft.com/en-us/library/azure/dn905915.aspx
A
References:
https://msdn.microsoft.com/en-us/library/azure/dn905915.aspx
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Question #7
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?
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?
- AYes
- BNo
Correct Answer:
B
References:
https://msdn.microsoft.com/en-us/library/azure/dn905915.aspx
B
References:
https://msdn.microsoft.com/en-us/library/azure/dn905915.aspx
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Question #8
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 save the output of the Score Model modules as a combined set, and then use the Project Columns module to select the MAE.
Does this meet the goal?
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 save the output of the Score Model modules as a combined set, and then use the Project Columns module to select the MAE.
Does this meet the goal?
- AYes
- BNo
Correct Answer:
B
References:
https://msdn.microsoft.com/en-us/library/azure/dn905915.aspx
B
References:
https://msdn.microsoft.com/en-us/library/azure/dn905915.aspx
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Question #9
You have data about the following:
✑ Users
✑ Movies
✑ User ratings of the movies
You need to predict whether a user will like a particular movie.
Which Matchbox recommender should you use?
✑ Users
✑ Movies
✑ User ratings of the movies
You need to predict whether a user will like a particular movie.
Which Matchbox recommender should you use?
- AItem Recommendation
- BRelated Items
- CRating Prediction
- DRelated Users
Correct Answer:
C
References:
https://msdn.microsoft.com/en-us/library/azure/dn905970.aspx#RatingPredictionOptions
C
References:
https://msdn.microsoft.com/en-us/library/azure/dn905970.aspx#RatingPredictionOptions
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Question #10
You have the following three training datasets for a restaurant:
✑ User features
✑ Item features
✑ Ratings of items by users
You must recommend restaurant to a particular user based only on the users features.
You need to use a Matchbox Recommender to make recommendations.
How many input parameters should you specify?
✑ User features
✑ Item features
✑ Ratings of items by users
You must recommend restaurant to a particular user based only on the users features.
You need to use a Matchbox Recommender to make recommendations.
How many input parameters should you specify?
- A1
- B2
- C3
- D4
Correct Answer:
B
References:
https://msdn.microsoft.com/en-us/library/azure/dn905987.aspx
B
References:
https://msdn.microsoft.com/en-us/library/azure/dn905987.aspx
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