70-774 Real Exam Questions

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 GPU Solution: You create an Azure notebook that supports the Microsoft Cognitive Toolkit. Does this meet the goal?

You are working on an Azure Machine Learning experiment that uses four different logistic regression algorithms. You are evaluating the algorithms based on the data in the following table. Which model produces predictions that are the closest to the actual outcomes?

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 norigin) 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?

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. You plan to create a predictive analytics solution for credit risk assessment and fraud prediction in Azure Machine Learning. The Machine Learning workspace for the solution will be shared with other users in your organization. You will add assets to projects and conduct experiments in the workspace. The experiments will be used for training models that will be published to provide scoring from web services. The experiment for fraud prediction will use Machine Learning modules and APIs to train the models and will predict probabilities in an Apache Hadoop ecosystem. You need to alter the list of columns that will be used for predicting fraud for an input web service endpoint. The columns from the original data source must be retained while running the Machine Learning experiment. Which module should you add after the web service input module and before the prediction module?

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 norigin) 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?