NEW QUESTION 1
You are building an Azure Machine Learning experiment.
You need to transform a string column into a label column for a Multiclass Decision Jungle module. Which module should you use?

• A. Select Columns Transform
• B. Group Categorical Values
• C. Convert to Indicator Values
• D. Edit Metadata

Answer: D

NEW QUESTION 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.
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.
The image files to train the object recognition model are stored in a Microsoft SQL Server 2021 Standard edition database on an Azure virtual machine (VM).
You need to support R packages that can use full parallel threading and processing for RevoScaleR.
How should you implement R? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION 3
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. K-Means Clustering
• C. Normalize Data
• D. Group Data into Bins

Answer: A

NEW QUESTION 4
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 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?

• A. clustering
• B. linear regression
• C. classification
• D. anomaly detection

Answer: C

Explanation: References:
https://gallery.cortanaintelligence.com/Experiment/Binary-Classification-Flight-delay-prediction-3

NEW QUESTION 5
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. Yes
• B. No

Answer: B

NEW QUESTION 6
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 preprocess the training data by using a Principal Component Analysis (PCA) algorithm in the least amount of time possible. Which implementation method should you use?

• A. Azure HDInsight using HiveML
• B. Azure Machine Learning Studio and a custom Execute Python Script module
• C. Azure HDInsight using Microsoft R Server
• D. Azure Machine Learning Studio with a custom Execute R Script module

Answer: C

NEW QUESTION 7
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 web service input to retrieve the data for the data source, and then add the Execute R Script module.
Does this meet the goal?

• A. Yes
• B. No

Answer: B

NEW QUESTION 8
You are building an Azure Machine Learning experiment.
You need to transform a string column that has 47 distinct values into a binary indicator column. The solution must use the One-vs-All Multiclass model.
Which module should you use?

• A. Select Column Transform
• B. Convert to Indicator Values
• C. Group Categorical Values
• D. Edit Metadata

Answer: B

NEW QUESTION 9
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. OriginAirportID
• B. DepDel
• C. DepDel30
• D. Carrier
• E. DestAirportID

Answer: C

NEW QUESTION 10
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. Tune Model Hyperparameters
• C. Normalize Data
• D. Select Columns in Dataset
• E. Import Data
• F. Edit Metadata
• G. Clip Values
• H. Clean Missing Data

Answer: A

Explanation: References:
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/sample-data-hive

NEW QUESTION 11
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 scries.
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.
You plan to share the Machine Learning workspace with the other users.
You are evaluating whether to assign the User role or the Owner role to several of the users.
Which three actions can be performed by the users who are assigned the User role? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

• A. Create, open, modify, and delete datasets.
• B. Create, open, modify, and delete experiments.
• C. Invite users to the workspace.
• D. Delete users from the workspace.
• E. Create, open, modify, and delete web services.
• F. Access notebooks.

Answer: CDF

NEW QUESTION 12
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 ensure that a web service endpoint can receive image data and use an object recognition model to return the expected object and the confidence level of the model. The solution must minimize the effort required to generate the client code to access the web service.
Which resource should you use?

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

Answer: B

NEW QUESTION 13
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. Boosted Decision Tree Regression model
• C. Two-Class boosted decision tree
• D. K-Means Clustering

Answer: A

NEW QUESTION 14
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. Cross-Validate Model
• B. Evaluate Model
• C. Tune Model Hyperparameters
• D. Train Model
• E. Score Model

Answer: A

Explanation: References:
https://msdn.microsoft.com/en-us/library/azure/dn905852.aspx

NEW QUESTION 15
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: In Azure Mt Studio, you use the Save as dataset option, and then open the output in a new notebook. Does this meet the goal?

• A. Yes
• B. No

Answer: A

NEW QUESTION 16
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 finish training the model and are ready to publish a predictive web service that will provide the users with the ability to specify the data source and the save location of the results. The model includes a Split Data module.
Which two actions should you perform to convert the Machine Learning experiment to a predictive web service? To answer, drag the appropriate actions to the correct targets. Each action may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation: References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/convert-training-experiment-to-scoring-experim

NEW QUESTION 17
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 endpoint to the Computer vision API. Does this meet the goal?

• A. Yes
• B. No

Answer: B