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NEW QUESTION 1
You have data about the following:
• Users
• Movies
• User ratings of the movies
You need to predict whether a user will like a particular movie. Which Matchbox recommender should you use?
- A. Rating Prediction
- B. Related Users
- C. Item Recommendation
- D. Related Items
Answer: A
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.
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 3
You plan to use Azure Machine Learning to develop a predictive model. You plan to include an Execute Python Script module.
What capability does the module provide?
- A. importing Python modules from a ZIP file for execution in a Machine Learning experiment
- B. performing interactive debugging of a Python script
- C. saving the results of a Python script run in a Machine Learning environment to a local file
- D. returning multiple data frames
Answer: A
NEW QUESTION 4
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 5
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. Evaluate Model
- B. Import Data
- C. Select Columns in Dataset
- D. Export Data
- E. Time Series Anomaly Detection
Answer: ABD
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.
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 7
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 8
You work for a company that has retail department stores.
You are developing an Azure Machine Learning experiment to predict seasonal sales. You need to address a model overfitting issue by using the following two solutions:
• Solution 1: Controls the penalty for complexity, which, when successful, prevents overfitting
• Solution 2: Separates model selection from testing, causing a more conservative estimate of generalization Which method should you use for each solution? To answer, drag the appropriate methods to the correct
solutions. Each method 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: 
NEW QUESTION 9
You plan to use the Import Data module to import data from a web URL by using HTTP. Which file format can you use as the source of the import operation?
- A. Optimized Row Columnar (ORQ
- B. Extensible Markup Language (XML)
- C. comma-separated value (CSV)
- D. JavaScript Object Notation (JSON)
Answer: D
NEW QUESTION 10
You need to use R code in a Transact-SQL statement to merge the repeating values 1 through 6 with Col1 in a table.
Which statement should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Answer:
Explanation: References:
https://docs.microsoft.com/en-us/sql/advanced-analytics/tutorials/rtsql-r-and-sql-data-types-and-data-objects
NEW QUESTION 11
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
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.
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 13
From the Cortana Intelligence Gallery, you deploy a solution. You need to modify the solution.
What should you use?
- A. Azure Stream Analytics
- B. Microsoft Power BI Desktop
- C. Azure Machine Learning Studio
- D. R Tools for Visual Studio
Answer: C
Explanation: References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/gallery-experiments
NEW QUESTION 14
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 connect the Score Model modules from each trained model as inputs for the Evaluate Model
module, and use the Execute R Script module.
Does this meet the goal?
- A. Yes
- B. No
Answer: A
Explanation: References:
https://msdn.microsoft.com/en-us/library/azure/dn905915.aspx
NEW QUESTION 15
You plan to use the Data Science Virtual Machine for development, but you are unfamiliar with R scripts. You need to generate R code for an experiment.
Which IDE should you use?
- A. XgBoost
- B. Rattle
- C. Vowpal Wabbit
- D. R Tools for Visual Studio
Answer: C
Explanation: References:
https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/provision-vm
NEW QUESTION 16
You have the following HiveQL query in an Import Data module.
Which type of operation is being performed?
- A. sampling a bucketized table
- B. random sampling by groups
- C. uniform random sampling
- D. stratified sampling
Answer: D
NEW QUESTION 17
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 have a dataset that contains a column named Column1. Column1 is empty. You need to omit Column1 from the dataset. The solution must use a native module. 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: D
Explanation: References:
https://msdn.microsoft.com/en-us/library/azure/dn905883.aspx
NEW QUESTION 18
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
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