70-774 試験問題を無料オンラインアクセス

You are building an Azure Machine Learning experiment.
You are preparing the output of a Boosted Decision Tree Regression module. You add a Normalize Data module to the experiment.
You need to ensure that the range of the transformation method produces an output on a scale of -1 to 1.
Which transformation method should you use?

• A.Zscore
• B.TanH
• C.MinMax
• D.Logistic

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 use only one percent of an Apache Hive data table by conducting random sampling by groups.
Which module should you use?

• A.Execute Python Script
• B.Clip Values
• C.Tune Model Hyperparameters
• D.Select Columns in Dataset
• E.Import Data
• F.Edit Metadata
• G.Normalize Data
• H.Clean Missing Data

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 an Azure notebook that supports the Microsoft Cognitive Toolkit.
Does this meet the goal?

• A.No
• B.Yes

You have an Azure Machine Learning experiment.
You discover that a model causes many errors in a production dataset. The model causes only few errors in the training data.
What is the cause of the errors?

• A.a simple predictor
• B.overfitting
• C.generalization
• D.underfitting

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 change a column name to a friendly name. The solution must use a native module. Which module should you use?

• A.Execute Python Script
• B.Clip Values
• C.Tune Model Hyperparameters
• D.Select Columns in Dataset
• E.Import Data
• F.Edit Metadata
• G.Normalize Data
• H.Clean Missing Data