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1. Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.

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 APL

Does this meet the goal?

2. Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.

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 Azure notebook that supports the Microsoft Cognitive Toolkit.

Does this meet the goal?

3. Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.

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 a Machine Learning Experiment that implements the Multiclass Neural Network Module.

Does this meet the goal?

4. Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.

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 a Machine Learning Experiment that implements the Multiclass Decision Jungle Module.

Does this meet the goal?

5. Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.

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?

6. Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.

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 result as a dataset.

Does this meet the goal?

7. Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.

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 use the Execute R Script Module.

Does this meet the goal?

8. Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.

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 modules to select the MAE.

Does this meet the goal?

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?

10. You have the following three training datasets for a restaurant:

* User Feature

* Item feature

* Ratings of items by users

You must recommend restaurants 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?

11. DRAG DROP

You have an Execute R Script module that has one input from either a Partition and Sample module or a Web Service input module.

You need to preprocess tweets by using R. The Solution must meet the following requirements:

* Remove digit

* Remove punctuation

* Convert to lowercase

How should you complete the R code? To answer drag the appropriate value to correct Target.

12. Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.

Start of repeated Scenario:

A Travel agency named Margie’s Travel sells airline tickets to customers in the United States.

Margie’s Travel wants you to provide insights and predictions on flight delays. The agency is considering implementing a system that will communicate to its customers as the flight departure near about possible delays due to weather conditions.

The flight data contains the following attributes:

* DepartureDate: The departure date aggregated at a per hour granularity.

* Carrier: The code assigned by the IATA and commonly used to identify a carrier.

* OriginAirportID: An identification number assigned by the USDOT to identify a unique airport (the flight’s Origin)

* DestAirportID: The departure delay in minutes.

*DepDet30: A Boolean value indicating whether the departure was delayed by 30 minutes or more ( a value of 1 indicates that the departure was delayed by 30 minutes or more)

The weather data contains the following Attributes: AirportID, ReadingDate (YYYY/MM/DD HH), SKYConditionVisibility, WeatherType, Windspeed, StationPressure, PressureChange and HourlyPrecip.

End of repeated Scenario:

You need to use historical data about on-time flight performance and the weather data to predict whether the departure of a scheduled flight will be delayed by more than 30 minutes.

Which method should you use?

13. DRAG DROP

Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.

Start of repeated Scenario:

A Travel agency named Margie’s Travel sells airline tickets to customers in the United States.

Margie’s Travel wants you to provide insights and predictions on flight delays. The agency is considering implementing a system that will communicate to its customers as the flight departure near about possible delays due to weather conditions.

The flight data contains the following attributes:

* DepartureDate: The departure date aggregated at a per hour granularity.

* Carrier: The code assigned by the IATA and commonly used to identify a carrier.

* OriginAirportID: An identification number assigned by the USDOT to identify a unique airport (the flight’s Origin)

* DestAirportID: The departure delay in minutes.

*DepDet30: A Boolean value indicating whether the departure was delayed by 30 minutes or more ( a value of 1 indicates that the departure was delayed by 30 minutes or more)

The weather data contains the following Attributes: AirportID, ReadingDate (YYYY/MM/DD HH), SKYConditionVisibility, WeatherType, Windspeed, StationPressure, PressureChange and HourlyPrecip.

End of repeated Scenario:

You need to remove the bias and to identify the columns in the input dataset that have the greatest predictive power.

Which module should you use for each requirement? To answer drag the appropriate modules to the correct requirements.

14. Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.

Start of repeated Scenario:

A Travel agency named Margie’s Travel sells airline tickets to customers in the United States.

Margie’s Travel wants you to provide insights and predictions on flight delays. The agency is considering implementing a system that will communicate to its customers as the flight departure near about possible delays due to weather conditions.

The flight data contains the following attributes:

* DepartureDate: The departure date aggregated at a per hour granularity.

* Carrier: The code assigned by the IATA and commonly used to identify a carrier.

* OriginAirportID: An identification number assigned by the USDOT to identify a unique airport (the flight’s Origin)

* DestAirportID: The departure delay in minutes.

*DepDet30: A Boolean value indicating whether the departure was delayed by 30 minutes or more ( a value of 1 indicates that the departure was delayed by 30 minutes or more)

The weather data contains the following Attributes: AirportID, ReadingDate (YYYY/MM/DD HH), SKYConditionVisibility, WeatherType, Windspeed, StationPressure, PressureChange and HourlyPrecip.

End of repeated Scenario:

You have an untrained Azure Machine Learning model that you plan to train to predict flight delays.

You need to assess the variability of the dataset and the reliability of the predictions from the model.

Which modules should you use?

15. Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.

Start of repeated Scenario:

A Travel agency named Margie’s Travel sells airline tickets to customers in the United States.

Margie’s Travel wants you to provide insights and predictions on flight delays. The agency is considering implementing a system that will communicate to its customers as the flight departure near about possible delays due to weather conditions.

The flight data contains the following attributes:

* DepartureDate: The departure date aggregated at a per hour granularity.

* Carrier: The code assigned by the IATA and commonly used to identify a carrier.

* OriginAirportID: An identification number assigned by the USDOT to identify a unique airport (the flight’s Origin)

* DestAirportID: The departure delay in minutes.

*DepDet30: A Boolean value indicating whether the departure was delayed by 30 minutes or more ( a value of 1 indicates that the departure was delayed by 30 minutes or more)

The weather data contains the following Attributes: AirportID, ReadingDate (YYYY/MM/DD HH), SKYConditionVisibility, WeatherType, Windspeed, StationPressure, PressureChange and HourlyPrecip.

End of repeated Scenario:

You plan to predict flight delays that are 30 minutes or more.

You need to build a training model that accurately fits the data. The solution must minimize over fitting and minimize data leakage. Which attribute should you remove?

16. Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.

You need to remove rows that have an empty value in a specific column. The solution must use a native module. Which module should you use?

17. Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.

You have a non-tabular file that is saved in Azure Blob Storage.

You need to download the file locally, access the data in the file, and then format the data as a dataset.

Which module should you use?

18. Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.

You have a dataset that contains a column named Column1. Column1 is empty.

You need to omit Column1 from the dataset. The solution must use a native module. Which module should you use?

19. Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.

You need to use only one percent of an Apache hive Data table by conducting random sampling by groups.

Which module should you use?