Online Access Free 70-774 Practice Test

You are building an Azure Machine Learning experiment.
You need to transform 47 numeric columns into a set of 10 linearly uncorrelated features.
Which module should you add to the experiment?

• A.Principal Component Analysis
• B.Group Data into Bins
• C.K-Means Clustering
• D.Normalize 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 section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Azure ML experiment that contains an intermediate dataset.
You need to explore data from the intermediate dataset by using Jupyter.
Solution: You add a Convert to ARFF module, and then add the Execute R Script module.
Does this meet the goal?

• A.No
• B.Yes

You are building an Azure Machine Learning solution for an online retailer.
When a customer selects a product, you need to recommend products that the customer might like to purchase at the same time. The recommendation should be based on what other customers purchased when they purchased the same product.
Which model should you use?

• A.Collaborative filtering
• B.Two-Class boosted decision tree
• C.K-Means Clustering
• D.Boosted Decision Tree Regression model

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 consolidate the output of the Score Model modules by using the Add Rows module, and then use the Execute R Script module.
Does this meet the goal?

• A.No
• B.Yes

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 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.
The users will use different data sources that follow a standard format. The users will receive results in a standard format by using the fraud prediction web service. The results will be saved to a location specified by the users.
You need to provide the users with the ability to get results for different risk tolerances without affecting the calculation of the model. Which three modules should be configured to use the Web Service Parameters? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

• A.Time Series Anomaly Detection
• B.Select Columns in Dataset
• C.Import Data
• D.Evaluate Model
• E.Export Data