本番さながらの環境で練習したい方には、JPNTestのテストエンジンがおすすめです。070-774試験の画面構成や制限時間を再現した模擬試験で、Microsoft Perform Cloud Data Science with Azure Machine Learningの65の問題に取り組めます。本番の緊張感に慣れておけば、当日も落ち着いて解答できるようになります。

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070-774試験についてよくあるご質問

Microsoft Perform Cloud Data Science with Azure Machine Learning 認定 070-774 試験問題：

問題 #1

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

問題 #2

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.
End of repeated scenario.
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?

A. SMOTE
B. Import Data
C. Select Columns in Dataset
D. Edit Metadata

問題 #3

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 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. DepDel
B. DepDel30
C. OriginAirportID
D. DestAirportID
E. Carrier

問題 #4

You are building an Azure Machine Learning workflow by using Azure Machine Learning Studio.
You create an Azure notebook that supports the Microsoft Cognitive Toolkit.
You need to ensure that the stochastic gradient descent (SGD) configuration maximizes the samples per second and supports parallel modeling that is managed by a parameter server.
Which SGD algorithm should you use?

A. BlockMomentumSGD
B. ModelAveragingSGD
C. DataParallelSGD
D. DataParallelASGD

問題 #5

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. MinMax
B. TanH
C. Zscore
D. Logistic
E. LogNormal

解説：