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											Q1.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.

[PIC-1]

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?
 - A:   Yes
 - B:   No

 solution: B

Explanation:
References:
https://msdn.microsoft.com/en-us/library/azure/dn905915.aspx


Q2.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.

[PIC-2]

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?
 - A:   Yes
 - B:   No

 solution: A

Explanation:
References:
https://msdn.microsoft.com/en-us/library/azure/dn905915.aspx


Q3.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.

[PIC-3]

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

 solution: B

Explanation:
References:
https://msdn.microsoft.com/en-us/library/azure/dn905915.aspx


Q4.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.

[PIC-4]

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?
 - A:   Yes
 - B:   No

 solution: B

Explanation:
References:
https://msdn.microsoft.com/en-us/library/azure/dn905915.aspx


Q5.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?
 - A:   Item Recommendation
 - B:   Related Items
 - C:   Rating Prediction
 - D:   Related Users

 solution: C

Explanation:
References:
https://msdn.microsoft.com/en-us/library/azure/dn905970.aspx#RatingPredictionOptions


Q6.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?
 - A:   1
 - B:   2
 - C:   3
 - D:   4

 solution: B

Explanation:
References:
https://msdn.microsoft.com/en-us/library/azure/dn905987.aspx


Q7.Note: This question is part of a series of questions that use the same scenario. For your convenience, the
scenario is repeated in each question. Each question presents a different goal and answer choices, but the text
of the scenario is exactly the same in each question in this series.
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 nears 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: An identification number assigned by the USDOT to identify a unique airport (the flight's
destination)
DepDel: The departure delay in minutes
DepDel30: 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.
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?
 - A:   clustering
 - B:   linear regression
 - C:   classification
 - D:   anomaly detection

 solution: C

Explanation:
References:
https://gallery.cortanaintelligence.com/Experiment/Binary-Classification-Flight-delay-prediction-3


Q8.Note: This question is part of a series of questions that use the same scenario. For your convenience, the
scenario is repeated in each question. Each question presents a different goal and answer choices, but the text
of the scenario is exactly the same in each question in this series.
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 nears 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
OriginAitportID: An identification number assigned by the USDOT to identify a unique airport (the flight's
origin)
DestAirportID: An identification number assigned by the USDOT to identify a unique airport (the flight's
destination)
DepDel: The departure delay in minutes
DepDel30: 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.
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 module should you use?
 - A:   Cross-Validate Model
 - B:   Evaluate Model
 - C:   Tune Model Hyperparameters
 - D:   Train Model
 - E:   Score Model

 solution: A

Explanation:
References:
https://msdn.microsoft.com/en-us/library/azure/dn905852.aspx


Q9.Note: This question is part of a series of questions that use the same scenario. For your convenience, the
scenario is repeated in each question. Each question presents a different goal and answer choices, but the text
of the scenario is exactly the same in each question in this series.
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 nears 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
OriginAitportID: An identification number assigned by the USDOT to identify a unique airport (the flight's
origin)
DestAirportID: An identification number assigned by the USDOT to identify a unique airport (the flight's
destination)
DepDel: The departure delay in minutes
DepDel30: 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.
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?
 - A:   OriginAirportID
 - B:   DepDel
 - C:   DepDel30
 - D:   Carrier
 - E:   DestAirportID

 solution: C



Q10.Note: This question is part of a series of questions that use the same or similar answer choices. An answer
choice may be correct for more than one question in the series. Each question is independent of the other
questions in this series. Information and details provided in a question apply only to that question.
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?
 - A:   Execute Python Script
 - B:   Tune Model Hyperparameters
 - C:   Normalize Data
 - D:   Select Columns in Dataset
 - E:   Import Data
 - F:   Edit Metadata
 - G:   Clip Values
 - H:   Clean Missing Data

 solution: H

Explanation:
References:
https://blogs.msdn.microsoft.com/azuredev/2017/05/27/data-cleansing-tools-in-azure-machine-learning/