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:
Solution: You create a Machine Learning experiment that implements the Multiclass Decision Jungle module.
Does this meet the goal?

• A. No
• B. Yes

You need to integrate code and formatted text into an Azure Machine Learning experiment that enables interactive execution.
What should you use?

• A. an Execute Python Script module
• B. an Execute R Script module
• C. a Jupyter notebook
• D. Azure Stream Analytics

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?

• A. No
• B. Yes

解説: (GoShiken メンバーにのみ表示されます)

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.
Start of repeated scenario
You plan to use Azure platform tools to detect and analyze food items in smart refrigerators. To provide families with an integrated experience for grocery shopping and cooking, the refrigerators will connect to other smart appliances, such as stoves and microwave ovens, on a LAN.
You plan to build an object recognition model by using the Microsoft Cognitive Toolkit. The object recognition model will receive input from the connected devices and send results to applications.
The training data will be derived from more than 10 TB of images. You will convert the raw images to the sparse format.
End of repeated scenario.
You need to deploy a multiple-service solution that was developed already and published by other users in the Microsoft development community.
What should you use?

• A. the edX Data Science Learning Dashboard
• B. an Azure Machine Learning experiment
• C. Cortana Intelligence Gallery
• D. the Data Science Virtual Machine

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:
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. Train Model
• B. Tune Model Hyperparameters
• C. Score Model
• D. Evaluate Model
• E. Cross-Validate Model

解説: (GoShiken メンバーにのみ表示されます)