70-774Preview

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.
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You are designing an Azure Machine Learning workflow.
You have a dataset that contains two million large digital photographs.
You plan to detect the presence of trees in the photographs.
You need to ensure that your model supports the following:
✑ Hidden layers that support a directed graph structure
✑ User-defined core components on the GPU
Solution: You create an endpoint to the Computer vision API.
Does this meet the goal?

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 consolidate the output of the Score Model modules by using the Add Rows module, and then use the Execute R Script module.
Does this meet the goal?

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?

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?

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?

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?

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?