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 digits.✑ Remove punctuation.✑ Convert to lowercase.How should you complete the R code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all.You may need to drag the split bar panes or scroll to view content.NOTE: Each correct selection is worth one point.Select and Place:
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 Margies Travel sells airline tickets to customers in the United States.Margies 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 flights origin)✑ DestAirportID: An identification number assigned by the USDOT to identify a unique airport (the flights 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?
DRAG DROP -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 Margies Travel sells airline tickets to customers in the United States.Margies 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 flights origin)✑ DestAirportID: An identification number assigned by the USDOT to identify a unique airport (the flights 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 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. Each module may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.NOTE: Each correct selection is worth one point.Select and Place:
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 Margies Travel sells airline tickets to customers in the United States.Margies 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 flights origin)✑ DestAirportID: An identification number assigned by the USDOT to identify a unique airport (the flights 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?
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 Margies Travel sells airline tickets to customers in the United States.Margies 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 flights origin)✑ DestAirportID: An identification number assigned by the USDOT to identify a unique airport (the flights 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?
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 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 GPUSolution: You create an endpoint to the Computer vision API.Does this meet the goal?
